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Machine learning in mental health: a scoping review of methods and applications

Published online by Cambridge University Press:  12 February 2019

Adrian B. R. Shatte Open the ORCID record for Adrian B. R. Shatte [Opens in a new window] ,
Delyse M. Hutchinson Open the ORCID record for Delyse M. Hutchinson [Opens in a new window]  and
Samantha J. Teague Open the ORCID record for Samantha J. Teague [Opens in a new window]
Adrian B. R. Shatte*
Affiliation:
Federation University, School of Science, Engineering & Information Technology, Melbourne, Australia Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
Delyse M. Hutchinson
Affiliation:
Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia Murdoch Children's Research Institute, Centre for Adolescent Health, Royal Children's Hospital, Melbourne, Australia Department of Paediatrics, University of Melbourne, Royal Children's Hospital, Melbourne, Australia University of New South Wales, National Drug and Alcohol Research Centre, Sydney, Australia
Samantha J. Teague
Affiliation:
Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Faculty of Health, Geelong, Australia
*
Author for correspondence: Adrian B. R. Shatte, E-mail: a.shatte@federation.edu.au
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Abstract

Background

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

Methods

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

Results

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

Conclusions

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Keywords

Big datahealth informaticsmachine learningmental health
Type
Review Article
Information
Psychological Medicine , Volume 49 , Issue 9 , July 2019 , pp. 1426 - 1448
Copyright
Copyright © Cambridge University Press 2019 

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References

Abou-Warda, H, Belal, NA, El-Sonbaty, Y and Darwish, S (2017) A random forest model for mental disorders diagnostic systems. In Hassanien, A, Shaalan, K, Gaber, T, Azar, A and Tolba, M (eds), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Cham: Springer, pp. 670–680.Google Scholar
Agarwal, A, Baechle, C, Behara, RS and Rao, V (2016) Multi-method approach to wellness predictive modeling. Journal of Big Data 3, 15.Google Scholar
Aguilar-Ruiz, JS, Costa, R and Divina, F (2004) Knowledge discovery from doctor-patient relationship. In Proceedings of the 2004 ACM Symposium on Applied Computing SAC ’04. New York, NY, USA: ACM, pp. 280–284.Google Scholar
Alam, MGR, Cho, EJ, Huh, EN and Hong, CS (2014) Cloud based mental state monitoring system for suicide risk reconnaissance using wearable bio-sensors. In Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication ICUIMC ’14, Siem Reap, Cambodia. New York, NY, USA: ACM, pp. 56:1–56:6.Google Scholar
Alam, MGR, Abedin, SF, Al Ameen, M and Hong, CS (2016) Web of objects based ambient assisted living framework for emergency psychiatric state prediction. Sensors 16, 1431.Google Scholar
Alexeeff, SE, Yau, V, Qian, Y, Davignon, M, Lynch, F, Crawford, P, Davis, R and Croen, LA (2017) Medical conditions in the first years of life associated with future diagnosis of ASD in children. Journal of Autism and Developmental Disorders 47, 2067–2079.Google Scholar
Alharthi, R, Alharthi, R, Guthier, B and El Saddik, A (2017) CASP: context-aware stress prediction system. Multimedia Tools and Applications Available at https://doi.org/10.1007/s11042-017-5246-0.Google Scholar
Almeida, H, Briand, A and Meurs, M-J (2017 a) Detecting early risk of depression from social media user-generated content. In Proceedings Conference and Labs of the Evaluation Forum (CLEF), Dublin, Ireland.Google Scholar
Almeida, H, Queudot, M, Kosseim, L and Meurs, M-J (2017 b) Supervised methods to support online scientific data triage. In Aïmeur, E, Ruhi, U and Weiss, M (eds), E-Technologies: Embracing the Internet of Things. MCETECH 2017. Lecture Notes in Business Information Processing, vol 289. Cham: Springer, pp. 213–221.Google Scholar
Amminger, GP, Mechelli, A, Rice, S, Kim, S-W, Klier, CM, McNamara, RK, Berk, M, McGorry, PD and Schäfer, MR (2015) Predictors of treatment response in young people at ultra-high risk for psychosis who received long-chain omega-3 fatty acids. Translational Psychiatry 5, e495.Google Scholar
Anderson, JP, Icten, Z, Alas, V, Benson, C and Joshi, K (2017) Comparison and predictors of treatment adherence and remission among patients with schizophrenia treated with paliperidone palmitate or atypical oral antipsychotics in community behavioral health organizations. BMC Psychiatry 17, 346.Google Scholar
Andrews, JA, Harrison, RF, Brown, LJE, MacLean, LM, Hwang, F, Smith, T, Williams, EA, Timon, C, Adlam, T, Khadra, H and Astell, AJ (2017) Using the NANA toolkit at home to predict older adults’ future depression. Journal of Affective Disorders 213, 187–190.Google Scholar
Anticevic, A, Cole, MW, Repovs, G, Murray, JD, Brumbaugh, MS, Winkler, AM, Savic, A, Krystal, JH, Pearlson, GD and Glahn, DC (2014) Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cerebral Cortex 24, 3116–3130.Google Scholar
Arksey, H and O'Malley, L (2005) Scoping studies: towards a methodological framework. International Journal of Social Research Methodology 8, 19–32.Google Scholar
Atkins, DC, Steyvers, M, Imel, ZE and Smyth, P (2014) Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implementation Science 9, 49.Google Scholar
Auer, M and Griffiths, MD (2018) Cognitive dissonance, personalized feedback, and online gambling behavior: an exploratory study using objective tracking data and subjective self-report. International Journal of Mental Health and Addiction 16, 631–641.Google Scholar
Azar, G, Gloster, C, El-Bathy, N, Yu, S, Neela, RH and Alothman, I (2015) Intelligent data mining and machine learning for mental health diagnosis using genetic algorithm. In 2015 IEEE International Conference on Electro/Information Technology (EIT). IEEE, pp. 201–206.Google Scholar
Baca-García, E, Perez-Rodriguez, MM, Basurte-Villamor, I, Saiz-Ruiz, J, Leiva-Murillo, JM, de Prado-Cumplido, M, Santiago-Mozos, R, Artés-Rodríguez, A and de Leon, J (2006) Using data mining to explore complex clinical decisions: a study of hospitalization after a suicide attempt. The Journal of Clinical Psychiatry 67, 1124–1132.Google Scholar
Bae, S, Chung, T, Ferreira, D, Dey, AK and Suffoletto, B (2018 a) Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: implications for just-in-time adaptive interventions. Addictive Behaviors 83, 42–47.Google Scholar
Bae, Y, Kumarasamy, K, Ali, IM, Korfiatis, P, Akkus, Z and Erickson, BJ (2018 b) Differences between schizophrenic and normal subjects using network properties from fMRI. Journal of Digital Imaging 31, 252–261.Google Scholar
Bailey, NW, Hoy, KE, Rogasch, NC, Thomson, RH, McQueen, S, Elliot, D, Sullivan, CM, Fulcher, BD, Daskalakis, ZJ and Fitzgerald, PB (2018) Responders to rTMS for depression show increased fronto-midline theta and theta connectivity compared to non-responders. Brain Stimulation 11, 190–203.Google Scholar
Bak, N, Ebdrup, BH, Oranje, B, Fagerlund, B, Jensen, MH, Düring, SW, Nielsen, MØ, Glenthøj, BY and Hansen, LK (2017) Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology. Translational Psychiatry 7, e1087.Google Scholar
Bang, S, Son, S, Roh, H, Lee, J, Bae, S, Lee, K, Hong, C and Shin, H (2017) Quad-phased data mining modeling for dementia diagnosis. BMC Medical Informatics and Decision Making 17, 60.Google Scholar
Banos, O, Bilal Amin, M, Ali Khan, W, Afzal, M, Hussain, M, Kang, BH and Lee, S (2016) The Mining Minds digital health and wellness framework. Biomedical Engineering Online 15(suppl. 1), 76.Google Scholar
Barros, J, Morales, S, Echávarri, O, García, A, Ortega, J, Asahi, T, Moya, C, Fischman, R, Maino, MP and Núñez, C (2017) Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders. Revista Brasileira de Psiquiatria 39, 1–11.Google Scholar
Bedi, G, Cecchi, GA, Slezak, DF, Carrillo, F, Sigman, M and de Wit, H (2014) A window into the intoxicated mind? Speech as an index of psychoactive drug effects. Neuropsychopharmacology 39, 2340–2348.Google Scholar
Bendfeldt, K, Smieskova, R, Koutsouleris, N, Klöppel, S, Schmidt, A, Walter, A, Harrisberger, F, Wrege, J, Simon, A, Taschler, B, Nichols, T, Riecher-Rössler, A, Lang, UE, Radue, E-W and Borgwardt, S (2015) Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing. NeuroImage. Clinical 9, 555–563.Google Scholar
Bermejo, P, Lucas, M, Rodríguez-Montes, JA, Tárraga, PJ, Lucas, J, Gámez, JA and Puerta, JM (2013) Single- and multi-label prediction of burden on families of schizophrenia patients. In Peek, N, Marín, Morales R and Peleg, M (eds), Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science, vol 7885. Berlin, Heidelberg: Springer, pp. 115–124.Google Scholar
Besga, A, Gonzalez, I, Echeburua, E, Savio, A, Ayerdi, B, Chyzhyk, D, Madrigal, JLM, Leza, JC, Graña, M and Gonzalez-Pinto, AM (2015) Discrimination between Alzheimer's disease and late onset bipolar disorder using multivariate analysis. Frontiers in Aging Neuroscience 7, 231.Google Scholar
Beykikhoshk, A, Arandjelovic, O, Phung, D, Venkatesh, S and Caelli, T (2015) Using Twitter to learn about the autism community. Social Network Analysis and Mining 5, 22.Google Scholar
Bhagyashree, SIR, Nagaraj, K, Prince, M, Fall, CHD and Krishna, M (2018) Diagnosis of dementia by machine learning methods in epidemiological studies: a pilot exploratory study from south India. Social Psychiatry and Psychiatric Epidemiology 53, 77–86.Google Scholar
Bleich-Cohen, M, Jamshy, S, Sharon, H, Weizman, R, Intrator, N, Poyurovsky, M and Hendler, T (2014) Machine learning fMRI classifier delineates subgroups of schizophrenia patients. Schizophrenia Research 160, 196–200.Google Scholar
Block, M, Stern, DB, Raman, K, Lee, S, Carey, J, Humphreys, AA, Mulhern, F, Calder, B, Schultz, D, Rudick, CN, Blood, AJ and Breiter, HC (2014) The relationship between self-report of depression and media usage. Frontiers in Human Neuroscience 8, 712.Google Scholar
Bone, D, Bishop, SL, Black, MP, Goodwin, MS, Lord, C and Narayanan, SS (2016) Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. Journal of Child Psychology and Psychiatry, and Allied Disciplines 57, 927–937.Google Scholar
Bone, D, Lee, CC, Chaspari, T, Gibson, J and Narayanan, S (2017) Processing and machine learning for mental health research and clinical applications. IEEE Signal Processing Magazine [Perspectives] 34, 189–195.Google Scholar
Bosl, WJ, Loddenkemper, T and Nelson, CA (2017) Nonlinear EEG biomarker profiles for autism and absence epilepsy. Neuropsychiatric Electrophysiology 3, 1.Google Scholar
Brasil Filho, AT, Pinheiro, PR and Coelho, A (2009) Towards the early diagnosis of Alzheimer's disease via a multicriteria classification model. In Ehrgott M, Fonseca CM, Gandibleux X, Hao JK and Sevaux M (eds), Evolutionary Multi-Criterion Optimization. EMO 2009. Lecture Notes in Computer Science, vol 5467. Berlin, Heidelberg: Springer.Google Scholar
Broek, EL, Sluis, F and Dijkstra, T (2013) Cross-validation of bimodal health-related stress assessment. Personal and Ubiquitous Computing 17, 215–227.Google Scholar
Bruining, H, Eijkemans, MJ, Kas, MJ, Curran, SR, Vorstman, JA and Bolton, PF (2014) Behavioral signatures related to genetic disorders in autism. Molecular Autism 5, 11.Google Scholar
Burnham, SC, Faux, NG, Wilson, W, Laws, SM, Ames, D, Bedo, J, Bush, AI, Doecke, JD, Ellis, KA, Head, R, Jones, G, Kiiveri, H, Martins, RN, Rembach, A, Rowe, CC, Salvado, O, Macaulay, SL, Masters, CL, Villemagne, VL and Alzheimer's Disease Neuroimaging Initiative, Australian Imaging, Biomarkers and Lifestyle Study Research Group (2014) A blood-based predictor for neocortical Aβ burden in Alzheimer's disease: results from the AIBL study. Molecular Psychiatry 19, 519–526.Google Scholar
Burns, MN, Begale, M, Duffecy, J, Gergle, D, Karr, CJ, Giangrande, E and Mohr, DC (2011) Harnessing context sensing to develop a mobile intervention for depression. Journal of Medical Internet Research 13, e55.Google Scholar
Caballero, FF, Soulis, G, Engchuan, W, Sánchez-Niubó, A, Arndt, H, Ayuso-Mateos, JL, Haro, JM, Chatterji, S and Panagiotakos, DB (2017) Advanced analytical methodologies for measuring healthy ageing and its determinants, using factor analysis and machine learning techniques: the ATHLOS project. Scientific Reports 7, 43955.Google Scholar
Cao, L, Guo, S, Xue, Z, Hu, Y, Liu, H, Mwansisya, TE, Pu, W, Yang, B, Liu, C, Feng, J, Chen, EYH and Liu, Z (2014) Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry and Clinical Neurosciences 68, 110–119.Google Scholar
Cao, B, Zheng, L, Zhang, C, Yu, PS, Piscitello, A, Zulueta, J, Ajilore, O, Ryan, K and Leow, AD (2017) Deepmood: modeling mobile phone typing dynamics for mood detection. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, pp. 747–755.Google Scholar
Carpenter, KL, Sprechmann, P, Calderbank, R, Sapiro, G and Egger, HL (2016) Quantifying risk for anxiety disorders in preschool children: a machine learning approach. PloS One 11, e0165524.Google Scholar
Carron-Arthur, B, Reynolds, J, Bennett, K, Bennett, A and Griffiths, KM (2016) What's all the talk about? Topic modelling in a mental health Internet support group. BMC Psychiatry 16, 367.Google Scholar
Castellani, U, Rossato, E, Murino, V, Bellani, M, Rambaldelli, G, Tansella, M and Brambilla, P (2009) Local kernel for brains classification in schizophrenia. In Serra, R and Cucchiara, R (eds), AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science, vol 5883. Berlin, Heidelberg: Springer, pp. 112–121.Google Scholar
Castellani, U, Rossato, E, Murino, V, Bellani, M, Rambaldelli, G, Perlini, C, Tomelleri, L, Tansella, M and Brambilla, P (2012) Classification of schizophrenia using feature-based morphometry. Journal of Neural Transmission 119, 395–404.Google Scholar
Castillo, A, Castellanos, A and Tremblay, MC (2014) Improving case management via statistical text mining in a foster care organization. In Tremblay, MC, VanderMeer, D, Rothenberger, M, Gupta, A and Yoon, V (eds), Advancing the Impact of Design Science: Moving From Theory to Practice. DESRIST 2014. Lecture Notes in Computer Science, vol 8463. Cham: Springer, pp. 312–320.Google Scholar
Chakraborty, D, Tahir, Y, Yang, Z, Maszczyk, T, Dauwels, J, Thalmann, D, Thalmann, NM, Tan, B-L and Lee, J (2017) Assessment and prediction of negative symptoms of schizophrenia from RGB+ D movement signals. In 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP). Luton, United Kingdom: IEEE, pp. 1–6.Google Scholar
Chalmers, C, Hurst, W, Mackay, M and Fergus, P (2016) A smart health monitoring technology. In Huang DS, Bevilacqua V and Premaratne P (eds), Intelligent Computing Theories and Application. ICIC 2016. Lecture Notes in Computer Science, vol 9771. Cham: Springer, pp. 832–842.Google Scholar
Chary, M, Genes, N, Giraud-Carrier, C, Hanson, C, Nelson, LS and Manini, AF (2017) Epidemiology from tweets: estimating misuse of prescription opioids in the USA from social Media. Journal of Medical Toxicology 13, 278–286.Google Scholar
Chen, M, Mao, S and Liu, Y (2014) Big data: a survey. Mobile Networks and Applications 19, 171–209.Google Scholar
Chen, R and Herskovits, EH (2007) Clinical diagnosis based on Bayesian classification of functional magnetic-resonance data. Neuroinformatics 5, 178–188.Google Scholar
Chen, T, Zeng, D and Wang, Y (2015) Multiple kernel learning with random effects for predicting longitudinal outcomes and data integration. Biometrics 71, 918–928.Google Scholar
Chen, X, Liu, C, He, H, Chang, X, Jiang, Y, Li, Y, Duan, M, Li, J, Luo, C and Yao, D (2017 a) Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. Journal of Affective Disorders 217, 118–124.Google Scholar
Chen, Y, Yann, ML-J, Davoudi, H, Choi, J, An, A and Mei, Z (2017 b) Contrast pattern based collaborative behavior recommendation for life improvement. In Kim, J, Shim, K, Cao, L, Lee, JG, Lin, X and Moon, YS (eds), Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10235. Cham: Springer, pp. 106–118.Google Scholar
Chiang, H-S, Liu, L-C and Lai, C-Y (2013) The diagnosis of mental stress by using data mining technologies. In Park, J, Barolli, L, Xhafa, F and Jeong, HY (eds), Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Dordrecht: Springer, pp. 761–769.Google Scholar
Chomutare, T (2014) Text classification to automatically identify online patients vulnerable to depression. In Cipresso, P, Matic, A and Lopez, G (eds), Pervasive Computing Paradigms for Mental Health. MindCare 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 100. Cham: Springer, pp. 125–130.Google Scholar
Clark, SR, Schubert, KO and Baune, BT (2015) Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome. Journal of Neural Transmission 122, 155–169.Google Scholar
Cook, BL, Progovac, AM, Chen, P, Mullin, B, Hou, S and Baca-Garcia, E (2016) Novel use of Natural Language Processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Computational and Mathematical Methods in Medicine 2016, 8708434.Google Scholar
Costafreda, SG, Dinov, ID, Tu, Z, Shi, Y, Liu, C-Y, Kloszewska, I, Mecocci, P, Soininen, H, Tsolaki, M, Vellas, B, Wahlund, L-O, Spenger, C, Toga, AW, Lovestone, S and Simmons, A (2011 a) Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment. NeuroImage 56, 212–219.Google Scholar
Costafreda, SG, Fu, CHY, Picchioni, M, Toulopoulou, T, McDonald, C, Kravariti, E, Walshe, M, Prata, D, Murray, RM and McGuire, PK (2011 b) Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder. BMC Psychiatry 11, 18.Google Scholar
Cvetković, B, Gjoreski, M, Šorn, J, Maslov, P and Luštrek, M (2017) Monitoring physical activity and mental stress using wrist-worn device and a smartphone. In Altun, Y et al. (eds), Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Science, vol 10536. Cham: Springer, pp. 414–418.Google Scholar
Dabek, F and Caban, JJ (2015) A neural network based model for predicting psychological conditions. In Guo, Y, Friston, K, Aldo, F, Hill, S and Peng, H (eds), Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science, vol 9250. Cham: Springer, pp. 252–261.Google Scholar
Dao, B, Nguyen, T, Phung, D and Venkatesh, S (2014) Effect of mood, social connectivity and age in online depression community via topic and linguistic analysis. In Benatallah, B, Bestavros, A, Manolopoulos, Y, Vakali, A and Zhang, Y (eds), Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8786. Cham: Springer, pp. 398–407.Google Scholar
Dao, B, Nguyen, T, Venkatesh, S and Phung, D (2016) Effect of social capital on emotion, language style and latent topics in online depression community. In 2016 IEEE RIVF International Conference on Computing Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), Hanoi, pp. 61–66.Google Scholar
Dao, B, Nguyen, T, Venkatesh, S and Phung, D (2017) Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities. International Journal of Data Science and Analytics 4, 209–231.Google Scholar
Deetjen, U and Powell, JA (2016) Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis. Journal of the American Medical Informatics Association: JAMIA 23, 508–513.Google Scholar
DeMasi, O and Recht, B (2017) A step towards quantifying when an algorithm can and cannot predict an individual's wellbeing. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers UbiComp ’17. New York, NY, USA: ACM, pp. 763–771.Google Scholar
Deng, F, Wang, Y, Huang, H, Niu, M, Zhong, S, Zhao, L, Qi, Z, Wu, X, Sun, Y, Niu, C, He, Y, Huang, L and Huang, R (2018) Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Progress in Neuro-Psychopharmacology & Biological Psychiatry 81, 340–349.Google Scholar
Dimitriadis, SI, Liparas, D, Tsolaki, MN and Alzheimer's Disease Neuroimaging Initiative (2018) Random forest feature selection, fusion and ensemble strategy: combining multiple morphological MRI measures to discriminate among healthy elderly, MCI, cMCI and Alzheimer's disease patients: from the Alzheimer's disease neuroimaging initiative (ADNI) database. Journal of Neuroscience Methods 302, 14–23.Google Scholar
Diniz, BS, Sibille, E, Ding, Y, Tseng, G, Aizenstein, HJ, Lotrich, F, Becker, JT, Lopez, OL, Lotze, MT, Klunk, WE, Reynolds, CF and Butters, MA (2015) Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. Molecular Psychiatry 20, 594–601.Google Scholar
Diniz, BS, Lin, C-W, Sibille, E, Tseng, G, Lotrich, F, Aizenstein, HJ, Reynolds, CF and Butters, MA (2016) Circulating biosignatures of late-life depression (LLD): towards a comprehensive, data-driven approach to understanding LLD pathophysiology. Journal of Psychiatric Research 82, 1–7.Google Scholar
Dipnall, JF, Pasco, JA, Berk, M, Williams, LJ, Dodd, S, Jacka, FN and Meyer, D (2016 a) Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression. PLoS One 11, e0148195.Google Scholar
Dipnall, JF, Pasco, JA, Berk, M, Williams, LJ, Dodd, S, Jacka, FN and Meyer, D (2016 b) Into the bowels of depression: unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample. PLoS One 11, e0167055.Google Scholar
Dipnall, JF, Pasco, JA, Berk, M, Williams, LJ, Dodd, S, Jacka, FN and Meyer, D (2017 a) Getting RID of the blues: formulating a risk Index for depression (RID) using structural equation modeling. The Australian and New Zealand Journal of Psychiatry 51, 1121–1133.Google Scholar
Dipnall, JF, Pasco, JA, Berk, M, Williams, LJ, Dodd, S, Jacka, FN and Meyer, D (2017 b) Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM). European Psychiatry: The Journal of the Association of European Psychiatrists 39, 40–50.Google Scholar
Dluhoš, P, Schwarz, D, Cahn, W, van Haren, N, Kahn, R, Španiel, F, Horáček, J, Kašpárek, T and Schnack, H (2017) Multi-center machine learning in imaging psychiatry: a meta-model approach. NeuroImage 155, 10–24.Google Scholar
Dmitrzak-Weglarz, MP, Pawlak, JM, Maciukiewicz, M, Moczko, J, Wilkosc, M, Leszczynska-Rodziewicz, A, Zaremba, D and Hauser, J (2015) Clock gene variants differentiate mood disorders. Molecular Biology Reports 42, 277–288.Google Scholar
Doan, NT, Engvig, A, Zaske, K, Persson, K, Lund, MJ, Kaufmann, T, Cordova-Palomera, A, Alnæs, D, Moberget, T, Brækhus, A, Barca, ML, Nordvik, JE, Engedal, K, Agartz, I, Selbæk, G, Andreassen, OA, Westlye, LT and Alzheimer's Disease Neuroimaging Initiative (2017 a) Distinguishing early and late brain aging from the Alzheimer's disease spectrum: consistent morphological patterns across independent samples. NeuroImage 158, 282–295.Google Scholar
Doan, S, Ritchart, A, Perry, N, Chaparro, JD and Conway, M (2017 b) How do you #relax when you're #stressed? A content analysis and infodemiology study of stress-related tweets. JMIR Public Health and Surveillance 3, e35.Google Scholar
Dyrba, M, Ewers, M, Wegrzyn, M, Kilimann, I, Plant, C, Oswald, A, Meindl, T, Pievani, M, Bokde, ALW, Fellgiebel, A, Filippi, M, Hampel, H, Klöppel, S, Hauenstein, K, Kirste, T, Teipel, SJ and EDSD study group (2013) Robust automated detection of microstructural white matter degeneration in Alzheimer's disease using machine learning classification of multicenter DTI data. PLoS One 8, e64925.Google Scholar
Dyrba, M, Barkhof, F, Fellgiebel, A, Filippi, M, Hausner, L, Hauenstein, K, Kirste, T, Teipel, SJ and EDSD study group (2015) Predicting prodromal Alzheimer's disease in subjects with mild cognitive impairment using machine learning classification of multimodal multicenter diffusion-tensor and magnetic resonance imaging data. Journal of Neuroimaging 25, 738–747.Google Scholar
El Naqa, I and Murphy, MJ (2015) What is machine learning? In El Naqa, I, Li, R and Murphy, M (eds), Machine Learning in Radiation Oncology. Cham: Springer, pp. 3–11.Google Scholar
Er, F, Iscen, P, Sahin, S, Çinar, N, Karsidag, S and Goularas, D (2017) Distinguishing age-related cognitive decline from dementias: a study based on machine learning algorithms. Journal of Clinical Neuroscience 42, 186–192.Google Scholar
Erguzel, TT and Tarhan, N (2016) Machine learning approaches to predict repetitive transcranial magnetic stimulation treatment response in Major depressive disorder. In Bi, Y, Kapoor, S and Bhatia, R (eds), Proceedings of SAI Intelligent Systems Conference. (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 16. Cham: Springer, pp. 391–401.Google Scholar
Erguzel, TT, Ozekes, S, Sayar, GH, Tan, O and Tarhan, N (2015) A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder. Neurocomputing 161, 220–228.Google Scholar
Ertek, G, Tokdil, B and Günaydın, İ (2014) Risk Factors and Identifiers for Alzheimer's Disease: A Data Mining Analysis. In Perner P (ed.), Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2014. Lecture Notes in Computer Science, vol 8557. Cham: Springer.Google Scholar
Fabbri, C, Corponi, F, Albani, D, Raimondi, I, Forloni, G, Schruers, K, Kasper, S, Kautzky, A, Zohar, J, Souery, D, Montgomery, S, Cristalli, CP, Mantovani, V, Mendlewicz, J and Serretti, A (2018) Pleiotropic genes in psychiatry: calcium channels and the stress-related FKBP5 gene in antidepressant resistance. Progress in Neuro-Psychopharmacology & Biological Psychiatry 81, 203–210.Google Scholar
Faedda, GL, Ohashi, K, Hernandez, M, McGreenery, CE, Grant, MC, Baroni, A, Polcari, A and Teicher, MH (2016) Actigraph measures discriminate pediatric bipolar disorder from attention-deficit/hyperactivity disorder and typically developing controls. Journal of Child Psychology and Psychiatry, and Allied Disciplines 57, 706–716.Google Scholar
Falahati, F, Ferreira, D, Soininen, H, Mecocci, P, Vellas, B, Tsolaki, M, Kłoszewska, I, Lovestone, S, Eriksdotter, M, Wahlund, L-O, Simmons, A, Westman, E and AddNeuroMed consortium and the Alzheimer's Disease Neuroimaging Initiative (2016) The effect of Age correction on multivariate classification in Alzheimer's disease, with a focus on the characteristics of incorrectly and correctly classified subjects. Brain Topography 29, 296–307.Google Scholar
Farhan, AA, Lu, J, Bi, J, Russell, A, Wang, B and Bamis, A (2016) Multi-view bi-clustering to identify smartphone sensing features indicative of depression. In 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 264–273.Google Scholar
Fraser, KC, Meltzer, JA and Rudzicz, F (2016) Linguistic features identify Alzheimer's disease in narrative speech. Journal of Alzheimer's Disease: JAD 49, 407–422.Google Scholar
Fung, G, Deng, Y, Zhao, Q, Li, Z, Qu, M, Li, K, Zeng, Y-W, Jin, Z, Ma, Y-T, Yu, X, Wang, Z-R, Shum, DHK and Chan, RCK (2015) Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study. BMC Psychiatry 15, 298.Google Scholar
Fusar-Poli, P, Rutigliano, G, Stahl, D, Schmidt, A, Ramella-Cravaro, V, Hitesh, S and McGuire, P (2016) Deconstructing pretest risk enrichment to optimize prediction of psychosis in individuals at clinical high risk. JAMA Psychiatry 73, 1260–1267.Google Scholar
Galiatsatos, D, Konstantopoulou, G, Anastassopoulos, G, Nerantzaki, M, Assimakopoulos, K and Lymberopoulos, D (2015) Classification of the most significant psychological symptoms in mental patients with depression using Bayesian network. In Proceedings of the 16th International Conference on Engineering Applications of Neural Networks (INNS) EANN ’15. New York, NY, USA: ACM, pp. 15:1–15:8.Google Scholar
Geraci, J, Wilansky, P, de Luca, V, Roy, A, Kennedy, JL and Strauss, J (2017) Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression. Evidence-Based Mental Health 20, 83–87.Google Scholar
Ghafoor, Y, Huang, Y-P and Liu, S-I (2015) An intelligent approach to discovering common symptoms among depressed patients. Springer Soft Computing 19, 819–827.Google Scholar
Gjoreski, M, Gjoreski, H, Luštrek, M and Gams, M (2016) Continuous stress detection using a wrist device: in laboratory and real life. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct UbiComp ’16. New York, NY, USA: ACM, pp. 1185–1193.Google Scholar
Glasgow, K, Fink, C and Boyd-Graber, JL (2014) ‘Our Grief is Unspeakable’: Automatically Measuring the Community Impact of a Tragedy. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, pp. 161–169.Google Scholar
Glasgow, K, Vitak, J, Tausczik, Y and Fink, C (2016) ‘With your help… we begin to heal’: social media expressions of gratitude in the aftermath of disaster. In Xu, K, Reitter, D, Lee, D and Osgood, N (eds), Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science, vol 9708. Cham: Springer, pp. 226–236.Google Scholar
Goch, CJ, Oztan, B, Stieltjes, B, Henze, R, Hering, J, Poustka, L, Meinzer, H-P, Yener, B and Maier-Hein, KH (2013) Global changes in the connectome in autism Spectrum disorders. In Schultz, T, Nedjati-Gilani, G, Venkataraman, A, O'Donnell, L and Panagiotaki, E (eds), Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Cham: Springer, pp. 239–247.Google Scholar
Golbeck, J (2016) Detecting coping style from twitter. In Spiro, E and Ahn, YY (eds), Social Informatics. SocInfo 2016. Lecture Notes in Computer Science, vol 10046. Cham: Springer, pp. 454–467.Google Scholar
Gomez-Uribe, CA and Hunt, N (2015) The Netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 13:1–13:19.Google Scholar
Greenstein, D, Malley, JD, Weisinger, B, Clasen, L and Gogtay, N (2012) Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls. Frontiers in Psychiatry/Frontiers Research Foundation 3, 53.Google Scholar
Guan, Z, Li, A and Zhu, T (2015) Local regression transfer learning with applications to users’ psychological characteristics prediction. Springer Brain Informatics 2, 145–153.Google Scholar
Guilloux, J-P, Bassi, S, Ding, Y, Walsh, C, Turecki, G, Tseng, G, Cyranowski, JM and Sibille, E (2015) Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression. Neuropsychopharmacology 40, 701–710.Google Scholar
Guo, S, Palaniyappan, L, Yang, B, Liu, Z, Xue, Z and Feng, J (2014) Anatomical distance affects functional connectivity in patients with schizophrenia and their siblings. Schizophrenia Bulletin 40, 449–459.Google Scholar
Hagad, JL, Moriyama, K, Fukui, K and Numao, M (2014) Modeling work stress using heart rate and stress coping profiles. In Baldoni, M et al. (eds), Principles and Practice of Multi-Agent Systems. CMNA 2015, IWEC 2015, IWEC 2014. Lecture Notes in Computer Science, vol 9935. Cham: Springer, pp. 108–118.Google Scholar
Hajek, T, Cooke, C, Kopecek, M, Novak, T, Hoschl, C and Alda, M (2015) Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. Journal of Psychiatry & Neuroscience: JPN 40, 316–324.Google Scholar
Hajek, T, Franke, K, Kolenic, M, Capkova, J, Matejka, M, Propper, L, Uher, R, Stopkova, P, Novak, T, Paus, T, Kopecek, M, Spaniel, F and Alda, M (2017) Brain age in early stages of bipolar disorders or schizophrenia. Schizophrenia Bulletin 45, 190–198.Google Scholar
Halfon, S, Aydın Oktay, E and Salah, AA (2016) Assessing affective dimensions of play in psychodynamic child psychotherapy via text analysis. In Chetouani, M, Cohn, J and Salah, A (eds), Human Behavior Understanding. HBU 2016. Lecture Notes in Computer Science, vol 9997. Cham: Springer, pp. 15–34.Google Scholar
Hao, B, Li, L, Li, A and Zhu, T (2013) Predicting mental health status on social media. In Rau, PLP (ed.), Cross-Cultural Design. Cultural Differences in Everyday Life. CCD 2013. Lecture Notes in Computer Science, vol 8024. Berlin, Heidelberg: Springer, pp. 101–110.Google Scholar
Hao, B, Li, L, Gao, R, Li, A and Zhu, T (2014) Sensing Subjective Well-being from Social Media. In Śle¸zak, D, Schaefer, G, Vuong, ST and Kim, YS (eds), Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Cham: Springer.Google Scholar
Harikumar, H, Nguyen, T, Gupta, S, Rana, S, Kaimal, R and Venkatesh, S (2016 a) Understanding behavioral differences between short and long-term drinking abstainers from social media. In Li, J, Li, X, Wang, S, Li, J and Sheng, Q (eds), Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, vol 10086. Cham: Springer, pp. 520–533.Google Scholar
Harikumar, H, Nguyen, T, Rana, S, Gupta, S, Kaimal, R and Venkatesh, S (2016 b) Extracting key challenges in achieving sobriety through shared subspace learning. In Li, J, Li, X, Wang, S, Li, J and Sheng, Q (eds), Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, vol 10086. Cham: Springer, pp. 420–433.Google Scholar
Hellstrøm, T, Kaufmann, T, Andelic, N, Soberg, HL, Sigurdardottir, S, Helseth, E, Andreassen, OA and Westlye, LT (2017) Predicting outcome 12 months after mild traumatic brain injury in patients admitted to a neurosurgery service. Frontiers in Neurology 8, 125.Google Scholar
Hess, JL, Tylee, DS, Barve, R, de Jong, S, Ophoff, RA, Kumarasinghe, N, Tooney, P, Schall, U, Gardiner, E, Beveridge, NJ, Scott, RJ, Yasawardene, S, Perera, A, Mendis, J, Carr, V, Kelly, B, Cairns, M, Neurobehavioural Genetics Unit, Tsuang, MT and Glatt, SJ (2016) Transcriptome-wide mega-analyses reveal joint dysregulation of immunologic genes and transcription regulators in brain and blood in schizophrenia. Schizophrenia Research 176, 114–124.Google Scholar
Hettige, NC, Nguyen, TB, Yuan, C, Rajakulendran, T, Baddour, J, Bhagwat, N, Bani-Fatemi, A, Voineskos, AN, Mallar Chakravarty, M and De Luca, V (2017) Classification of suicide attempters in schizophrenia using sociocultural and clinical features: a machine learning approach. General Hospital Psychiatry 47, 20–28.Google Scholar
Hoogendoorn, M, Berger, T, Schulz, A, Stolz, T and Szolovits, P (2017) Predicting social anxiety treatment outcome based on therapeutic email conversations. IEEE Journal of Biomedical and Health Informatics 21, 1449–1459.Google Scholar
Hou, Y, Xu, J, Huang, Y and Ma, X (2016) A big data application to predict depression in the university based on the reading habits. In 2016 3rd International Conference on Systems and Informatics. (ICSAI), Shanghai, pp. 1085–1089.Google Scholar
Hu, B and Terrazas, BV (2016) Building a mental health knowledge model to facilitate decision support. In Ohwada, H and Yoshida, K (eds), Knowledge Management and Acquisition for Intelligent Systems. PKAW 2016. Lecture Notes in Computer Science, vol 9806. Cham: Springer, pp. 198–212.Google Scholar
Hutchinson, DM, Silins, E, Mattick, RP, Patton, GC, Fergusson, DM, Hayatbakhsh, R, Toumbourou, JW, Olsson, CA, Najman, JM, Spry, E, Tait, RJ, Degenhardt, L, Swift, W, Butterworth, P, Horwood, LJ and Cannabis Cohorts Research Consortium (2015) How can data harmonisation benefit mental health research? An example of the cannabis cohorts research consortium. The Australian and New Zealand Journal of Psychiatry 49, 317–323.Google Scholar
Iannaccone, R, Hauser, TU, Ball, J, Brandeis, D, Walitza, S and Brem, S (2015) Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. European Child & Adolescent Psychiatry 24, 1279–1289.Google Scholar
Iliou, T, Konstantopoulou, G, Ntekouli, M, Lymperopoulou, C, Assimakopoulos, K, Galiatsatos, D and Anastassopoulos, G (2017) ILIOU machine learning preprocessing method for depression type prediction. Evolving Systems 475, 53–60.Google Scholar
Iniesta, R, Malki, K, Maier, W, Rietschel, M, Mors, O, Hauser, J, Henigsberg, N, Dernovsek, MZ, Souery, D, Stahl, D, Dobson, R, Aitchison, KJ, Farmer, A, Lewis, CM, McGuffin, P and Uher, R (2016) Combining clinical variables to optimize prediction of antidepressant treatment outcomes. Journal of Psychiatric Research 78, 94–102.Google Scholar
Islam, J and Zhang, Y (2017) A novel deep learning based multi-class classification method for Alzheimer's disease detection using brain MRI data. In Zeng, Y et al. (eds), Brain Informatics. BI 2017. Lecture Notes in Computer Science, vol 10654. Cham: Springer, pp. 213–222.Google Scholar
Iwabuchi, SJ and Palaniyappan, L (2017) Abnormalities in the effective connectivity of visuothalamic circuitry in schizophrenia. Psychological Medicine 47, 1300–1310.Google Scholar
Iwabuchi, SJ, Liddle, PF and Palaniyappan, L (2013) Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging. Frontiers in Psychiatry/Frontiers Research Foundation 4, 95.Google Scholar
Jiao, Y, Chen, R, Ke, X, Chu, K, Lu, Z and Herskovits, EH (2010) Predictive models of autism spectrum disorder based on brain regional cortical thickness. NeuroImage 50, 589–599.Google Scholar
Jiao, Y, Chen, R, Ke, X, Cheng, L, Chu, K, Lu, Z and Herskovits, EH (2012) Single nucleotide polymorphisms predict symptom severity of autism spectrum disorder. Journal of Autism and Developmental Disorders 42, 971–983.Google Scholar
Jie, N-F, Osuch, EA, Zhu, M-H, Wammes, M, Ma, X-Y, Jiang, T-Z, Sui, J and Calhoun, VD (2018) Discriminating bipolar disorder from major depression using whole-brain functional connectivity: a feature selection analysis with SVM-FoBa algorithm. Journal of Signal Processing Systems 90, 259–271.Google Scholar
Jiménez-Serrano, S, Tortajada, S and García-Gómez, JM (2015) A mobile health application to predict postpartum depression based on machine learning. Telemedicine Journal and e-Health 21, 567–574.Google Scholar
Jin, H, Wu, S and Di Capua, P (2015) Development of a clinical forecasting model to predict comorbid depression among diabetes patients and an application in depression screening policy making. Preventing Chronic Disease 12, E142.Google Scholar
Jin, C, Jia, H, Lanka, P, Rangaprakash, D, Li, L, Liu, T, Hu, X and Deshpande, G (2017) Dynamic brain connectivity is a better predictor of PTSD than static connectivity. Human Brain Mapping 38, 4479–4496.Google Scholar
Johannesen, JK, Bi, J, Jiang, R, Kenney, JG and Chen, C-MA (2016) Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults. Neuropsychiatric Electrophysiology 2, 3.Google Scholar
Johansson, R, Sjöberg, E, Sjögren, M, Johnsson, E, Carlbring, P, Andersson, T, Rousseau, A and Andersson, G (2012) Tailored vs. Standardized internet-based cognitive behavior therapy for depression and comorbid symptoms: a randomized controlled trial. PLoS One 7, e36905.Google Scholar
Johnson, P, Vandewater, L, Wilson, W, Maruff, P, Savage, G, Graham, P, Macaulay, LS, Ellis, KA, Szoeke, C, Martins, RN, Rowe, CC, Masters, CL, Ames, D and Zhang, P (2014) Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease. BMC Bioinformatics 15(suppl. 16), S11.Google Scholar
Jordan, MI and Mitchell, TM (2015) Machine learning: trends, perspectives, and prospects. Science 349, 255–260.Google Scholar
Kamdar, MR and Wu, MJ (2016) PRISM: a data-driven platform for monitoring mental health. Pacific Symposium on Biocomputing 21, 333–344.Google Scholar
Kang, Y, Jiang, X, Yin, Y, Shang, Y and Zhou, X (2017) Deep transformation learning for depression diagnosis from facial images. In Zhou, J et al. (eds), Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science, vol 10568. Cham: Springer, pp. 13–22.Google Scholar
Karamzadeh, N, Amyot, F, Kenney, K, Anderson, A, Chowdhry, F, Dashtestani, H, Wassermann, EM, Chernomordik, V, Boccara, C, Wegman, E, Diaz-Arrastia, R and Gandjbakhche, AH (2016) A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near-infrared spectroscopy. Brain and Behavior 6, e00541.Google Scholar
Karstoft, K-I, Statnikov, A, Andersen, SB, Madsen, T and Galatzer-Levy, IR (2015) Early identification of posttraumatic stress following military deployment: application of machine learning methods to a prospective study of Danish soldiers. Journal of Affective Disorders 184, 170–175.Google Scholar
Karystianis, G, Nevado, AJ, Kim, C-H, Dehghan, A, Keane, JA and Nenadic, G (2018) Automatic mining of symptom severity from psychiatric evaluation notes. International Journal of Methods in Psychiatric Research 27, e1602.Google Scholar
Kaufmann, T, Alnæs, D, Brandt, CL, Doan, NT, Kauppi, K, Bettella, F, Lagerberg, TV, Berg, AO, Djurovic, S, Agartz, I, Melle, IS, Ueland, T, Andreassen, OA and Westlye, LT (2017) Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. NeuroImage 147, 243–252.Google Scholar
Kaufmann, T, Skåtun, KC, Alnæs, D, Doan, NT, Duff, EP, Tønnesen, S, Roussos, E, Ueland, T, Aminoff, SR, Lagerberg, TV, Agartz, I, Melle, IS, Smith, SM, Andreassen, OA and Westlye, LT (2015) Disintegration of sensorimotor brain networks in schizophrenia. Schizophrenia Bulletin 41, 1326–1335.Google Scholar
Kavuluru, R, Williams, AG, Ramos-Morales, M, Haye, L, Holaday, T and Cerel, J (2016) Classification of helpful comments on online suicide watch forums. ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2016, 32–40.Google Scholar
Kessler, RC, Rose, S, Koenen, KC, Karam, EG, Stang, PE, Stein, DJ, Heeringa, SG, Hill, ED, Liberzon, I, McLaughlin, KA, McLean, SA, Pennell, BE, Petukhova, M, Rosellini, AJ, Ruscio, AM, Shahly, V, Shalev, AY, Silove, D, Zaslavsky, AM, Angermeyer, MC, Bromet, EJ, de Almeida, JMC, de Girolamo, G, de Jonge, P, Demyttenaere, K, Florescu, SE, Gureje, O, Haro, JM, Hinkov, H, Kawakami, N, Kovess-Masfety, V, Lee, S, Medina-Mora, ME, Murphy, SD, Navarro-Mateu, F, Piazza, M, Posada-Villa, J, Scott, K, Torres, Y and Carmen Viana, M (2014) How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry 13, 265–274.Google Scholar
Kessler, RC, Warner, CH, Ivany, C, Petukhova, MV, Rose, S, Bromet, EJ, Brown, M III, Cai, T, Colpe, LJ, Cox, KL, Fullerton, CS, Gilman, SE, Gruber, MJ, Heeringa, SG, Lewandowski-Romps, L, Li, J, Millikan-Bell, AM, Naifeh, JA, Nock, MK, Rosellini, AJ, Sampson, NA, Schoenbaum, M, Stein, MB, Wessely, S, Zaslavsky, AM, Ursano, RJ and Army STARRS Collaborators (2015) Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army Study To Assess Risk and rEsilience in Servicemembers (Army STARRS). JAMA Psychiatry 72, 49–57.Google Scholar
Kessler, RC, van Loo, HM, Wardenaar, KJ, Bossarte, RM, Brenner, LA, Cai, T, Ebert, DD, Hwang, I, Li, J, de Jonge, P, Nierenberg, AA, Petukhova, MV, Rosellini, AJ, Sampson, NA, Schoevers, RA, Wilcox, MA and Zaslavsky, AM (2016) Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Molecular Psychiatry 21, 1366–1371.Google Scholar
Kessler, RC, Hwang, I, Hoffmire, CA, McCarthy, JF, Petukhova, MV, Rosellini, AJ, Sampson, NA, Schneider, AL, Bradley, PA, Katz, IR, Thompson, C and Bossarte, RM (2017 a) Developing a practical suicide risk prediction model for targeting high-risk patients in the Veterans health Administration. International Journal of Methods in Psychiatric Research 26, e1575.Google Scholar
Kessler, RC, Stein, MB, Petukhova, MV, Bliese, P, Bossarte, RM, Bromet, EJ, Fullerton, CS, Gilman, SE, Ivany, C, Lewandowski-Romps, L, Millikan Bell, A, Naifeh, JA, Nock, MK, Reis, BY, Rosellini, AJ, Sampson, NA, Zaslavsky, AM, Ursano, RJ and Army STARRS Collaborators (2017 b) Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Molecular Psychiatry 22, 544–551.Google Scholar
Khalilia, M, Chakraborty, S and Popescu, M (2011) Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making 11, 51.Google Scholar
Khondoker, M, Dobson, R, Skirrow, C, Simmons, A and Stahl, D (2016) A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies. Statistical Methods in Medical Research 25, 1804–1823.Google Scholar
Kim, H, Chun, H-W, Kim, S, Coh, B-Y, Kwon, O-J and Moon, Y-H (2017) Longitudinal study-based dementia prediction for public health. International Journal of Environmental Research and Public Health 14, 983.Google Scholar
Kliper, R, Portuguese, S and Weinshall, D (2016) Prosodic analysis of speech and the underlying mental state. In Serino, S, Matic, A, Giakoumis, D, Lopez, G and Cipresso, P (eds), Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Cham: Springer, pp. 52–62.Google Scholar
Klöppel, S, Peter, J, Ludl, A, Pilatus, A, Maier, S, Mader, I, Heimbach, B, Frings, L, Egger, K, Dukart, J, Schroeter, ML, Perneczky, R, Häussermann, P, Vach, W, Urbach, H, Teipel, S, Hüll, M, Abdulkadir, A and Alzheimer's Disease Neuroimaging Initiative (2015) Applying automated MR-based diagnostic methods to the memory clinic: a prospective study. Journal of Alzheimer's Disease: JAD 47, 939–954.Google Scholar
König, A, Satt, A, Sorin, A, Hoory, R, Toledo-Ronen, O, Derreumaux, A, Manera, V, Verhey, F, Aalten, P, Robert, PH and David, R (2015) Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease. Alzheimer's & Dementia: The Journal of the Alzheimer's Association 1, 112–124.Google Scholar
Koutsouleris, N, Meisenzahl, EM, Davatzikos, C, Bottlender, R, Frodl, T, Scheuerecker, J, Schmitt, G, Zetzsche, T, Decker, P, Reiser, M, Möller, H-J and Gaser, C (2009) Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Archives of General Psychiatry 66, 700–712.Google Scholar
Koutsouleris, N, Borgwardt, S, Meisenzahl, EM, Bottlender, R, Möller, H-J and Riecher-Rössler, A (2012) Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophrenia Bulletin 38, 1234–1246.Google Scholar
Koutsouleris, N, Davatzikos, C, Borgwardt, S, Gaser, C, Bottlender, R, Frodl, T, Falkai, P, Riecher-Rössler, A, Möller, H-J, Reiser, M, Pantelis, C and Meisenzahl, E (2014) Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophrenia Bulletin 40, 1140–1153.Google Scholar
Koutsouleris, N, Kahn, RS, Chekroud, AM, Leucht, S, Falkai, P, Wobrock, T, Derks, EM, Fleischhacker, WW and Hasan, A (2016) Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. The Lancet. Psychiatry 3, 935–946.Google Scholar
Koutsouleris, N, Wobrock, T, Guse, B, Langguth, B, Landgrebe, M, Eichhammer, P, Frank, E, Cordes, J, Wölwer, W, Musso, F, Winterer, G, Gaebel, W, Hajak, G, Ohmann, C, Verde, PE, Rietschel, M, Ahmed, R, Honer, WG, Dwyer, D, Ghaseminejad, F, Dechent, P, Malchow, B, Kreuzer, PM, Poeppl, TB, Schneider-Axmann, T, Falkai, P and Hasan, A (2018) Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis. Schizophrenia Bulletin 44, 1021–1034.Google Scholar
Kumari, RS, Sheela Kumari, R, Varghese, T, Kesavadas, C, Albert Singh, N and Mathuranath, PS (2013) A genetic algorithm optimized artificial neural network for the segmentation of MR images in frontotemporal dementia. In Panigrahi, BK, Suganthan, PN, Das, S and Dash, SS (eds), Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8298. Cham: Springer, pp. 268–276.Google Scholar
Labate, D, La Foresta, F, Palamara, I and Morabito, G (2014) EEG complexity modifications and altered compressibility in mild cognitive impairment and Alzheimer's disease. In Bassis, S, Esposito, A and Morabito, F (eds), Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Cham: Springer, pp. 163-173.Google Scholar
Lenhard, F, Sauer, S, Andersson, E, Månsson, KN, Mataix-Cols, D, Rück, C and Serlachius, E (2018) Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: a machine learning approach. International Journal of Methods in Psychiatric Research 27, e1576Google Scholar
Li, Q, Wu, X, Xu, L, Chen, K, Yao, L and Li, R (2017 a) Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment. Computer Methods and Programs in Biomedicine 150, 1–8.Google Scholar
Li, Q, Zhao, L, Xue, Y, Jin, L and Feng, L (2017 b) Exploring the impact of co-experiencing stressor events for teens stress forecasting. In Bouguettaya, A et al. (eds), Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science, vol 10570. Cham: Springer, pp. 313–328.Google Scholar
Liang, X, Gu, S, Deng, J, Gao, Z, Zhang, Z and Shen, D (2015) Investigation of college students’ mental health status via semantic analysis of Sina microblog. Wuhan University Journal of Natural Sciences 20, 159–164.Google Scholar
Liang, S, Brown, MRG, Deng, W, Wang, Q, Ma, X, Li, M, Hu, X, Juhas, M, Li, X, Greiner, R, Greenshaw, AJ and Li, T (2018 a) Convergence and divergence of neurocognitive patterns in schizophrenia and depression. Schizophrenia Research 192, 327–334.Google Scholar
Liang, S, Vega, R, Kong, X, Deng, W, Wang, Q, Ma, X, Li, M, Hu, X, Greenshaw, AJ, Greiner, R and Li, T (2018 b) Neurocognitive graphs of first-episode schizophrenia and major depression based on cognitive features. Neuroscience Bulletin 34, 312–320.Google Scholar
Liu, F, Guo, W, Fouche, J-P, Wang, Y, Wang, W, Ding, J, Zeng, L, Qiu, C, Gong, Q, Zhang, W and Chen, H (2015 a) Multivariate classification of social anxiety disorder using whole brain functional connectivity. Brain Structure & Function 220, 101–115.Google Scholar
Liu, F, Xie, B, Wang, Y, Guo, W, Fouche, J-P, Long, Z, Wang, W, Chen, H, Li, M, Duan, X, Zhang, J, Qiu, M and Chen, H (2015 b) Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach. Brain Topography 28, 221–237.Google Scholar
Liu, W, Li, M and Yi, L (2016) Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Research 9, 888–898.Google Scholar
Liu, Z, Tang, B, Wang, X and Chen, Q (2017) De-identification of clinical notes via recurrent neural network and conditional random field. Journal of Biomedical Informatics 75S, S34–S42.Google Scholar
Lord, A, Horn, D, Breakspear, M and Walter, M (2012) Changes in community structure of resting state functional connectivity in unipolar depression. PLoS One 7, e41282.Google Scholar
Lueken, U, Straube, B, Yang, Y, Hahn, T, Beesdo-Baum, K, Wittchen, H-U, Konrad, C, Ströhle, A, Wittmann, A, Gerlach, AL, Pfleiderer, B, Arolt, V and Kircher, T (2015) Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. Journal of Affective Disorders 184, 182–192.Google Scholar
Luo, J, Wu, M, Gopukumar, D and Zhao, Y (2016) Big data application in biomedical research and health care: a literature review. Biomedical Informatics Insights 8, 1–10.Google Scholar
Ma, L, Wang, Z and Zhang, Y (2017) Extracting depression symptoms from social networks and web blogs via text mining. In Cai, Z, Daescu, O and Li, M (eds), Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science, vol 10330. Cham: Springer, pp. 325–330.Google Scholar
Maraş, A and Aydin, S (2017) Discrimination of psychotic symptoms from controls through data mining methods based on emotional principle components. In CMBEBIH 2017. Singapore: Springer, pp. 26–30.Google Scholar
Maxhuni, A, Hernandez-Leal, P, Morales, EF, Enrique Sucar, L, Osmani, V, Muńoz-Meléndez, A and Mayora, O (2016) Using intermediate models and knowledge learning to improve stress prediction. In Sucar, E, Mayora, O, Munoz de Cote, E (eds), Applications for Future Internet. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 179. Cham: Springer, pp. 140–151.Google Scholar
Mechelli, A, Lin, A, Wood, S, McGorry, P, Amminger, P, Tognin, S, McGuire, P, Young, J, Nelson, B and Yung, A (2017) Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis. Schizophrenia Research 184, 32–38.Google Scholar
Metzger, M-H, Tvardik, N, Gicquel, Q, Bouvry, C, Poulet, E and Potinet-Pagliaroli, V (2017) Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study. International Journal of Methods in Psychiatric Research 26, e1522.Google Scholar
Meyer, D, Abbott, J-AM and Nedejkovic, M (2015) Big data study for coping with stress. In Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015), Sydney, Australia.Google Scholar
Mikolas, P, Melicher, T, Skoch, A, Matejka, M, Slovakova, A, Bakstein, E, Hajek, T and Spaniel, F (2016) Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study. Psychological Medicine 46, 2695–2704.Google Scholar
Mitra, V, Shriberg, E, McLaren, M, Kathol, A, Richey, C, Vergyri, D and Graciarena, M (2014) The SRI AVEC-2014 evaluation system. In Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge AVEC ’14. New York, NY, USA: ACM, pp. 93–101.Google Scholar
Mohammadi, M, Al-Azab, F, Raahemi, B, Richards, G, Jaworska, N, Smith, D, de la Salle, S, Blier, P and Knott, V (2015) Data mining EEG signals in depression for their diagnostic value. BMC Medical Informatics and Decision Making 15, 108.Google Scholar
Moher, D, Liberati, A, Tetzlaff, J, Altman, DG and PRISMA Group (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. International Journal of Surgery 8, 336–341.Google Scholar
Moulahi, B, Azé, J and Bringay, S (2017) DARE to care: a context-aware framework to track suicidal ideation on social media. In Bouguettaya, A et al. (eds), Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science, vol 10570. Cham: Springer, pp. 346–353.Google Scholar
Nahum-Shani, I, Smith, SN, Spring, BJ, Collins, LM, Witkiewitz, K, Tewari, A and Murphy, SA (2017) Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine 52, 446–462.Google Scholar
Nakashima, Y, Kim, J, Flutura, S, Seiderer, A and André, E (2016) Stress recognition in daily work. In Serino, S, Matic, A, Giakoumis, D, Lopez, G and Cipresso, P (eds), Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Cham: Springer, pp. 23–33.Google Scholar
Nandhini, BS and Sheeba, JI (2015) Cyberbullying detection and classification using information retrieval algorithm. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015) ICARCSET ’15. New York, NY, USA: ACM, pp. 20:1–20:5.Google Scholar
Nguyen, T, Duong, T, Phung, D and Venkatesh, S (2014 a) Affective, linguistic and topic patterns in online autism communities. In Benatallah, B, Bestavros, A, Manolopoulos, Y, Vakali, A and Zhang, Y (eds), Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Cham: Springer, pp. 474–488.Google Scholar
Nguyen, T, Phung, D, Dao, B, Venkatesh, S and Berk, M (2014 b) Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing 5, 217–226.Google Scholar
Nguyen, T, O'Dea, B, Larsen, M, Phung, D, Venkatesh, S and Christensen, H (2015) Differentiating sub-groups of online depression-related communities using textual cues. In Wang, J et al. (eds), Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science, vol 9419. Cham: Springer, pp. 216–224.Google Scholar
Nguyen, T, Borland, R, Yearwood, J, Yong, H-H, Venkatesh, S and Phung, D (2016 a) Discriminative cues for different stages of smoking cessation in online community. In Cellary, W, Mokbel, M, Wang, J, Wang, H, Zhou, R and Zhang, Y (eds), Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science, vol 10042. Cham: Springer, pp. 146–153.Google Scholar
Nguyen, T, Venkatesh, S and Phung, D (2016 b) Textual cues for online depression in community and personal settings. In Li, J, Li, X, Wang, S, Li, J and Sheng, Q (eds), Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science, vol 10086. Cham: Springer, pp. 19–34.Google Scholar
Nguyen, T, O'Dea, B, Larsen, M, Phung, D, Venkatesh, S and Christensen, H (2017) Using linguistic and topic analysis to classify sub-groups of online depression communities. Multimedia Tools and Applications 76, 10653–10676.Google Scholar
Nicodemus, KK, Callicott, JH, Higier, RG, Luna, A, Nixon, DC, Lipska, BK, Vakkalanka, R, Giegling, I, Rujescu, D, St Clair, D, Muglia, P, Shugart, YY and Weinberger, DR (2010) Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging. Human Genetics 127, 441–452.Google Scholar
Nikfarjam, A, Sarker, A, O'Connor, K, Ginn, R and Gonzalez, G (2015) Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association: JAMIA 22, 671–681.Google Scholar
O'Dea, B, Wan, S, Batterham, PJ, Calear, AL, Paris, C and Christensen, H (2015) Detecting suicidality on Twitter. Internet Interventions 2, 183–188.Google Scholar
O'Halloran, R, Kopell, BH, Sprooten, E, Goodman, WK and Frangou, S (2016) Multimodal neuroimaging-informed clinical applications in neuropsychiatric disorders. Frontiers in Psychiatry/Frontiers Research Foundation 7, 63.Google Scholar
Oh, DH, Kim, IB, Kim, SH and Ahn, DH (2017) Predicting autism spectrum disorder using blood-based gene expression signatures and machine learning. Clinical Psychopharmacology and Neuroscience 15, 47–52.Google Scholar
Ojeme, B and Mbogho, A (2016 a) Predictive strength of Bayesian networks for diagnosis of depressive disorders. In Czarnowski, I, Caballero, A, Howlett, R and Jain, L (eds), Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Cham: Springer, pp. 373–382.Google Scholar
Ojeme, B and Mbogho, A (2016 b) Selecting learning algorithms for simultaneous identification of depression and comorbid disorders. Procedia Computer Science 96, 1294–1303.Google Scholar
Oseguera, O, Rinaldi, A, Tuazon, J and Cruz, AC (2017) Automatic quantification of the veracity of suicidal ideation in counseling transcripts. In Stephanidis, C (ed.), HCI International 2017 – Posters’ Extended Abstracts. HCI 2017. Communications in Computer and Information Science, vol 713. Cham: Springer, pp. 473–479.Google Scholar
Pampouchidou, A, Pediaditis, M, Maridaki, A, Awais, M, Vazakopoulou, C-M, Sfakianakis, S, Tsiknakis, M, Simos, P, Marias, K, Yang, F and Meriaudeau, F (2017) Quantitative comparison of motion history image variants for video-based depression assessment. EURASIP Journal on Image and Video Processing 2017, 64.Google Scholar
Panagiotakopoulos, TC, Lyras, DP, Livaditis, M, Sgarbas, KN, Anastassopoulos, GC and Lymberopoulos, DK (2010) A contextual data mining approach toward assisting the treatment of anxiety disorders. IEEE Transactions on Information Technology in Biomedicine 14, 567–581.Google Scholar
Pandey, A, Davis, NA, White, BC, Pajewski, NM, Savitz, J, Drevets, WC and McKinney, BA (2012) Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder. Translational Psychiatry 2, e154.Google Scholar
Paredes, P, Gilad-Bachrach, R, Czerwinski, M, Roseway, A, Rowan, K and Hernandez, J (2014) Poptherapy: coping with stress through pop-culture. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare PervasiveHealth ’14. Brussels, Belgium, Belgium: ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 109–117.Google Scholar
Park, A, Conway, M and Chen, AT (2018) Examining thematic similarity, difference, and membership in three online mental health communities from Reddit: a text mining and visualization approach. Computers in Human Behavior 78, 98–112.Google Scholar
Parrado-Hernández, E, Gómez-Verdejo, V, Martinez-Ramon, M, Alonso, P, Pujol, J, Menchón, JM, Cardoner, N and Soriano-Mas, C (2012) Identification of OCD-relevant brain areas through multivariate feature selection. In Langs, G, Rish, I, Grosse-Wentrup, M and Murphy, B (eds), Machine Learning and Interpretation in Neuroimaging. Lecture Notes in Computer Science, vol 7263. Berlin, Heidelberg: Springer, pp. 60–67.Google Scholar
Pedersen, M, Curwood, EK, Archer, JS, Abbott, DF and Jackson, GD (2015) Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: multivariate analysis of task-free fMRI. Epilepsia 56, 1767–1773.Google Scholar
Peng, Z, Hu, Q and Dang, J (2019) Multi-kernel SVM based depression recognition using social media data. International Journal of Machine Learning and Cybernetics 10, 43–57.Google Scholar
Perlini, C, Bellani, M, Finos, L, Lasalvia, A, Bonetto, C, Scocco, P, D'Agostino, A, Torresani, S, Imbesi, M, Bellini, F, Konze, A, Veronese, A, Ruggeri, M, Brambilla, P and GET UP Group (2017) Non literal language comprehension in a large sample of first episode psychosis patients in adulthood. Psychiatry Research 260, 78–89.Google Scholar
Perlis, RH (2013) A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biological Psychiatry 74, 7–14.Google Scholar
Pestian, JP, Matykiewicz, P and Grupp-Phelan, J (2008) Using natural language processing to classify suicide notes. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing BioNLP ‘08. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 96–97.Google Scholar
Pestian, J, Nasrallah, H, Matykiewicz, P, Bennett, A and Leenaars, A (2010) Suicide note classification using natural language processing: a content analysis. Biomedical Informatics Insights 2010, 19–28.Google Scholar
Pestian, JP, Grupp-Phelan, J, Bretonnel Cohen, K, Meyers, G, Richey, LA, Matykiewicz, P and Sorter, MT (2016) A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department. Suicide & Life-Threatening Behavior 46, 154–159.Google Scholar
Pham, MT, Rajić, A, Greig, JD, Sargeant, JM, Papadopoulos, A and McEwen, SA (2014) A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Research Synthesis Methods 5, 371–385.Google Scholar
Plitt, M, Barnes, KA and Martin, A (2015) Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage. Clinical 7, 359–366.Google Scholar
Posada, JD, Barda, AJ, Shi, L, Xue, D, Ruiz, V, Kuan, P-H, Ryan, ND and Tsui, FR (2017) Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records. Journal of Biomedical Informatics 75S, S94–S104.Google Scholar
Poulin, C, Shiner, B, Thompson, P, Vepstas, L, Young-Xu, Y, Goertzel, B, Watts, B, Flashman, L and McAllister, T (2014) Predicting the risk of suicide by analyzing the text of clinical notes. PLoS One 9, e85733.Google Scholar
Qian, B, Wang, X, Cao, N, Li, H and Jiang, Y-G (2015) A relative similarity based method for interactive patient risk prediction. Data Mining and Knowledge Discovery 29, 1070–1093.Google Scholar
Rabbi, M, Ali, S, Choudhury, T and Berke, E (2011) Passive and In-situ assessment of mental and physical well-being using mobile sensors. In Proceedings of the 13th International Conference on Ubiquitous Computing UbiComp ’11. New York, NY, USA: ACM, pp. 385–394.Google Scholar
Rakshith, V, Apoorv, V, Akarsh, NK, Arjun, K, Krupa, BN, Pratima, M and Vedamurthachar, A (2017) A novel approach for the identification of chronic alcohol users from ECG signals. In TENCON 2017–2017 IEEE Region 10 Conference, IEEE Penang. pp. 1321–1326.Google Scholar
Ramasubbu, R, Brown, MRG, Cortese, F, Gaxiola, I, Goodyear, B, Greenshaw, AJ, Dursun, SM and Greiner, R (2016) Accuracy of automated classification of major depressive disorder as a function of symptom severity. NeuroImage: Clinical 12, 320–331.Google Scholar
Reece, AG and Danforth, CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Science 6, 15.Google Scholar
Rentoumi, V, Peters, T, Conlin, J and Garrard, P (2017) The acute mania of King George III: a computational linguistic analysis. PLoS One 12, e0171626.Google Scholar
Rikandi, E, Pamilo, S, Mäntylä, T, Suvisaari, J, Kieseppä, T, Hari, R, Seppä, M and Raij, TT (2017) Precuneus functioning differentiates first-episode psychosis patients during the fantasy movie Alice in Wonderland. Psychological Medicine 47, 495–506.Google Scholar
Roberts, G, Lord, A, Frankland, A, Wright, A, Lau, P, Levy, F, Lenroot, RK, Mitchell, PB and Breakspear, M (2017) Functional dysconnection of the inferior frontal gyrus in young people with bipolar disorder or at genetic high risk. Biological Psychiatry 81, 718–727.Google Scholar
Rosellini, AJ, Dussaillant, F, Zubizarreta, JR, Kessler, RC and Rose, S (2018) Predicting posttraumatic stress disorder following a natural disaster. Journal of Psychiatric Research 96, 15–22.Google Scholar
Ross, J, Neylan, T, Weiner, M, Chao, L, Samuelson, K and Sim, I (2015) Towards constructing a new taxonomy for psychiatry using self-reported symptoms. Studies in Health Technology and Informatics 216, 736–740.Google Scholar
Roysden, N and Wright, A (2015) Predicting health care utilization after behavioral health referral using natural language processing and machine learning. Annual Symposium Proceedings/AMIA Symposium. AMIA Symposium 2015, 2063–2072.Google Scholar
Rozycki, M, Satterthwaite, TD, Koutsouleris, N, Erus, G, Doshi, J, Wolf, DH, Fan, Y, Gur, RE, Gur, RC, Meisenzahl, EM, Zhuo, C, Ying, H, Yan, H, Yue, W, Zhang, D and Davatzikos, C (2018) Multisite machine learning analysis provides a robust structural imaging signature of schizophrenia detectable across diverse patient populations and within individuals. Schizophrenia Bulletin 44, 1035–1044.Google Scholar
Ryu, E, Takahashi, PY, Olson, JE, Hathcock, MA, Novotny, PJ, Pathak, J, Bielinski, SJ, Cerhan, JR and Sloan, JA (2015) Quantifying the importance of disease burden on perceived general health and depressive symptoms in patients within the Mayo Clinic Biobank. Health and Quality of Life Outcomes 13, 95.Google Scholar
Ryu, E, Chamberlain, AM, Pendegraft, RS, Petterson, TM, Bobo, WV and Pathak, J (2016) Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records. BMC Psychiatry 16, 114.Google Scholar
Saha, K and de Choudhury, M (2017) Modeling stress with social media around incidents of gun violence on college campuses. Proceedings of the ACM on Human-Computer Interaction (CSCW) 1, 92, 1–27.Google Scholar
Saha, B, Nguyen, T, Phung, D and Venkatesh, S (2016) A framework for classifying online mental health-related communities with an interest in depression. IEEE Journal of Biomedical and Health Informatics 20, 1008–1015.Google Scholar
Salafi, T and Kah, JCY (2015) Design of unobtrusive wearable mental stress monitoring device using physiological sensor. In Goh, J and Lim, C (eds), 7th WACBE World Congress on Bioengineering 2015. IFMBE Proceedings, vol 52. Cham: Springer, pp. 11–14.Google Scholar
Sandulescu, V, Andrews, S, Ellis, D, Bellotto, N and Mozos, OM (2015) Stress detection using wearable physiological sensors. In Ferrández, Vicente J, Álvarez-Sánchez, J, de la Paz López, F, Toledo-Moreo, F and Adeli, H (eds), Artificial Computation in Biology and Medicine. IWINAC 2015. Lecture Notes in Computer Science, vol 9107. Cham: Springer, pp. 526–532.Google Scholar
Sano, A, Phillips, AJ, Yu, AZ, McHill, AW, Taylor, S, Jaques, N, Czeisler, CA, Klerman, EB and Picard, RW (2015) Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using Personality Traits, Wearable Sensors and Mobile Phones. ieeexplore.ieee.org … International Conference on Wearable and Implantable Body Sensor Networks. International Conference on Wearable and Implantable Body Sensor Networks 2015.Google Scholar
Sato, JR, Moll, J, Green, S, Deakin, JFW, Thomaz, CE and Zahn, R (2015) Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Research 233, 289–291.Google Scholar
Sato, JR, Salum, GA, Gadelha, A, Crossley, N, Vieira, G, Manfro, GG, Zugman, A, Picon, FA, Pan, PM, Hoexter, MQ, Anés, M, Moura, LM, Del'Aquilla, MAG, Amaro, E Jr, McGuire, P, Lacerda, ALT, Rohde, LA, Miguel, EC, Jackowski, AP and Bressan, RA (2016) Default mode network maturation and psychopathology in children and adolescents. Journal of Child Psychology and Psychiatry, and Allied Disciplines 57, 55–64.Google Scholar
Sato, JR, Biazoli, CE, Salum, GA, Gadelha, A, Crossley, N, Vieira, G, Zugman, A, Picon, FA, Pan, PM, Hoexter, MQ, Amaro, E, Anés, M, Moura, LM, Del'Aquilla, MAG, Mcguire, P, Rohde, LA, Miguel, EC, Jackowski, AP and Bressan, RA (2018) Association between abnormal brain functional connectivity in children and psychopathology: a study based on graph theory and machine learning. The World Journal of Biological Psychiatry 19, 119–129.Google Scholar
Saxe, GN, Ma, S, Ren, J and Aliferis, C (2017) Machine learning methods to predict child posttraumatic stress: a proof of concept study. BMC Psychiatry 17, 223.Google Scholar
Sheela Kumari, R, Varghese, T, Kesavadas, C, Albert Singh, N and Mathuranath, PS (2014) Longitudinal evaluation of structural changes in frontotemporal dementia using artificial neural networks. In Satapathy, S, Udgata, S and Biswal, B (eds), Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Cham: Springer, pp. 165–172.Google Scholar
Shen, Y-C, Kuo, T-T, Yeh, I-N, Chen, T-T and Lin, S-D (2013) Exploiting temporal information in a Two-stage classification framework for content-based depression detection. In Pei, J, Tseng, VS, Cao, L, Motoda, H and Xu, G (eds), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol 7818. Berlin, Heidelberg: Springer, pp. 276–288.Google Scholar
Shiner, B, D'Avolio, LW, Nguyen, TM, Zayed, MH, Young-Xu, Y, Desai, RA, Schnurr, PP, Fiore, LD and Watts, BV (2013) Measuring use of evidence based psychotherapy for posttraumatic stress disorder. Administration and Policy in Mental Health 40, 311–318.Google Scholar
Siang Fook, VF, Jayachandran, M, Phyo Wai, AA, Tolstikov, A, Biswas, J and Lin Kiat, PY (2009) iCOPE: intelligent context-aware patient management systems for elderly with cognitive and functional impairment. In McClean, S, Millard, P, El-Darzi, E and Nugent, C (eds), Intelligent Patient Management. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 259–278.Google Scholar
Sidahmed, H, Prokofyeva, E and Blaschko, MB (2016) Discovering predictors of mental health service utilization with k-support regularized logistic regression. Information Sciences 329, 937–949.Google Scholar
Simms, T, Ramstedt, C, Rich, M, Richards, M, Martinez, T and Giraud-Carrier, C (2017) Detecting cognitive distortions through machine learning text analytics. In 2017 IEEE International Conference on Healthcare Informatics (ICHI), Park City, UT, pp. 508–512.Google Scholar
Skåtun, KC, Kaufmann, T, Doan, NT and Alnæs, D (2016) Consistent functional connectivity alterations in schizophrenia spectrum disorder: a multisite study. Schizophrenia 43, 914–924.Google Scholar
Smets, E, Casale, P, Großekathöfer, U, Lamichhane, B, De Raedt, W, Bogaerts, K, Van Diest, I and Van Hoof, C (2016) Comparison of machine learning techniques for psychophysiological stress detection. In Serino, S, Matic, A, Giakoumis, D, Lopez, G and Cipresso, P (eds), Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Cham: Springer, pp. 13–22.Google Scholar
Song, H, Du, W and Zhao, Q (2015) Automatic depression discrimination on FNIRS by using FastICA/WPD and SVM. In Proceedings of the 2015 Chinese Intelligent Automation Conference. Berlin, Heidelberg: Springer, pp. 257–265.Google Scholar
Song, I, Dillon, D, Goh, TJ and Sung, M (2011) A health social network recommender system. In Agents in Principle, Agents in Practice. Berlin, Heidelberg: Springer, pp. 361–372.Google Scholar
Souillard-Mandar, W, Davis, R, Rudin, C, Au, R, Libon, DJ, Swenson, R, Price, CC, Lamar, M and Penney, DL (2016) Learning classification models of cognitive conditions from subtle behaviors in the digital clock drawing test. Machine Learning 102, 393–441.Google Scholar
Squarcina, L, Perlini, C, Bellani, M, Lasalvia, A, Ruggeri, M, Brambilla, P and Castellani, U (2015 a) Learning with heterogeneous data for longitudinal studies. In Navab, N, Hornegger, J, Wells, W and Frangi, A (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham: Springer, pp. 535–542.Google Scholar
Squarcina, L, Perlini, C, Peruzzo, D, Castellani, U, Marinelli, V, Bellani, M, Rambaldelli, G, Lasalvia, A, Tosato, S, De Santi, K, Spagnolli, F, Cerini, R, Ruggeri, M, Brambilla, P and PICOS-Veneto Group (2015 b) The use of dynamic susceptibility contrast (DSC) MRI to automatically classify patients with first episode psychosis. Schizophrenia Research 165, 38–44.Google Scholar
Squeglia, LM, Ball, TM, Jacobus, J, Brumback, T, McKenna, BS, Nguyen-Louie, TT, Sorg, SF, Paulus, MP and Tapert, SF (2017) Neural predictors of initiating alcohol use during adolescence. The American Journal of Psychiatry 174, 172–185.Google Scholar
Stone, JR, Wilde, EA, Taylor, BA, Tate, DF, Levin, H, Bigler, ED, Scheibel, RS, Newsome, MR, Mayer, AR, Abildskov, T, Black, GM, Lennon, MJ, York, GE, Agarwal, R, DeVillasante, J, Ritter, JL, Walker, PB, Ahlers, ST and Tustison, NJ (2016) Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury. Brain Injury 30, 1458–1468.Google Scholar
Strous, RD, Koppel, M, Fine, J, Nachliel, S, Shaked, G and Zivotofsky, AZ (2009) Automated characterization and identification of schizophrenia in writing. The Journal of Nervous and Mental Disease 197, 585–588.Google Scholar
Stütz, T, Kowar, T, Kager, M, Tiefengrabner, M, Stuppner, M, Blechert, J, Wilhelm, FH and Ginzinger, S (2015) Smartphone based stress prediction. In Ricci, F, Bontcheva, K, Conlan, O and Lawless, S (eds), User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science, vol 9146. Cham: Springer, pp. 240–251.Google Scholar
Sun, B, Zhang, Z, Liu, X, Hu, B and Zhu, T (2017) Self-esteem recognition based on gait pattern using Kinect. Gait & Posture 58, 428–432.Google Scholar
Sundermann, B, Bode, J, Lueken, U, Westphal, D, Gerlach, AL, Straube, B, Wittchen, H-U, Ströhle, A, Wittmann, A, Konrad, C, Kircher, T, Arolt, V and Pfleiderer, B (2017) Support vector machine analysis of functional magnetic resonance imaging of interoception does not reliably predict individual outcomes of cognitive behavioral therapy in panic disorder with agoraphobia. Frontiers in Psychiatry/Frontiers Research Foundation 8, 99.Google Scholar
Takagi, Y, Sakai, Y, Lisi, G, Yahata, N, Abe, Y, Nishida, S, Nakamae, T, Morimoto, J, Kawato, M, Narumoto, J and Tanaka, SC (2017) A neural marker of obsessive-compulsive disorder from whole-brain functional connectivity. Scientific Reports 7, 7538.Google Scholar
Taylor, JA, Matthews, N, Michie, PT, Rosa, MJ and Garrido, MI (2017) Auditory prediction errors as individual biomarkers of schizophrenia. NeuroImage. Clinical 15, 264–273.Google Scholar
Teague, SJ and Shatte, ABR (2018) Exploring the transition to fatherhood: feasibility study using social media and machine learning. JMIR Pediatrics and Parenting 1, e12371.Google Scholar
Teague, S, Youssef, GJ, Macdonald, JA, Sciberras, E, Shatte, A, Fuller-Tyszkiewicz, M, Greenwood, C, McIntosh, J, Olsson, CA, Hutchinson, D and SEED Lifecourse Sciences Theme (2018) Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis. BMC Medical Research Methodology 18, 151.Google Scholar
Thin, N, Hung, N, Venkatesh, S and Phung, D (2017) Estimating support scores of autism communities in large-scale web information systems. In Bouguettaya, A et al. (eds), Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science, vol 10569. Cham: Springer, pp. 347–355.Google Scholar
Tran, T and Kavuluru, R (2017) Predicting mental conditions based on ‘history of present illness’ in psychiatric notes with deep neural networks. Journal of Biomedical Informatics 75S, S138–S148.Google Scholar
Tran, T, Phung, D, Luo, W, Harvey, R, Berk, M and Venkatesh, S (2013) An integrated framework for suicide risk prediction. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD ’13. New York, NY, USA: ACM, pp. 1410–1418.Google Scholar
Tran, T, Phung, D, Luo, W and Venkatesh, S (2015) Stabilized sparse ordinal regression for medical risk stratification. Knowledge & Information Systems 43, 555–582.Google Scholar
Tremblay, S, Iturria-Medina, Y, Mateos-Pérez, JM, Evans, AC and De Beaumont, L (2017) Defining a multimodal signature of remote sports concussions. The European Journal of Neuroscience 46, 1956–1967.Google Scholar
Tron, T, Peled, A, Grinsphoon, A and Weinshall, D (2016) Automated facial expressions analysis in schizophrenia: a continuous dynamic approach. In Serino, S, Matic, A, Giakoumis, D, Lopez, G and Cipresso, P (eds), Pervasive Computing Paradigms for Mental Health. MindCare 2015. Communications in Computer and Information Science, vol 604. Cham: Springer, pp. 72–81.Google Scholar
Vakorin, VA, Doesburg, SM, da Costa, L, Jetly, R, Pang, EW and Taylor, MJ (2016) Detecting mild traumatic brain injury using resting state magnetoencephalographic connectivity. PLoS Computational Biology 12, e1004914.Google Scholar
van Breda, W, Pastor, J, Hoogendoorn, M, Ruwaard, J, Asselbergs, J and Riper, H (2016) Exploring and comparing machine learning approaches for predicting mood over time. In Chen, YW, Tanaka, S, Howlett, R and Jain, L (eds), Innovation in Medicine and Healthcare 2016. InMed 2016. Smart Innovation, Systems and Technologies, vol 60. Cham: Springer, pp. 37–47.Google Scholar
Vandewater, L, Brusic, V, Wilson, W, Macaulay, L and Zhang, P (2015) An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression. BMC Bioinformatics 16(suppl. 18), S1.Google Scholar
Vigneron, V, Kodewitz, A, Tome, AM, Lelandais, S and Lang, E (2016) Alzheimer's disease brain areas: the machine learning support for blind localization. Current Alzheimer Research 13, 498–508.Google Scholar
Wahle, F, Kowatsch, T, Fleisch, E and Rufer, M (2016) Mobile sensing and support for people with depression: a pilot trial in the wild. JMIR mHealth and uHealth 4, e111.Google Scholar
Wang, S-H, Zhang, Y, Li, Y-J, Jia, W-J, Liu, F-Y, Yang, M-M and Zhang, Y-D (2018) Single slice based detection for Alzheimer's disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimedia Tools and Applications 77, 10393–10417.Google Scholar
Wang, Z, Shah, AD, Tate, AR, Denaxas, S, Shawe-Taylor, J and Hemingway, H (2012) Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLoS One 7, e30412.Google Scholar
Wang, X, Zhang, C, Ji, Y, Sun, L, Wu, L and Bao, Z (2013) A depression detection model based on sentiment analysis in micro-blog social network. In Li, J et al. (eds), Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol 7867. Berlin, Heidelberg: Springer, pp. 201–213.Google Scholar
Wang, Y, Iyengar, V, Hu, J, Kho, D, Falconer, E, Docherty, JP and Yuen, GY (2017) Predicting future high-cost schizophrenia patients using high-dimensional administrative data. Frontiers in Psychiatry/Frontiers Research Foundation 8, 114.Google Scholar
Wardenaar, KJ, van Loo, HM, Cai, T, Fava, M, Gruber, MJ, Li, J, de Jonge, P, Nierenberg, AA, Petukhova, MV, Rose, S, Sampson, NA, Schoevers, RA, Wilcox, MA, Alonso, J, Bromet, EJ, Bunting, B, Florescu, SE, Fukao, A, Gureje, O, Hu, C, Huang, YQ, Karam, AN, Levinson, D, Medina Mora, ME, Posada-Villa, J, Scott, KM, Taib, NI, Viana, MC, Xavier, M, Zarkov, Z and Kessler, RC (2014) The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity. Psychological Medicine 44, 3289–3302.Google Scholar
Westman, E, Aguilar, C, Muehlboeck, J-S and Simmons, A (2013) Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topography 26, 9–23.Google Scholar
Whelan, R, Watts, R, Orr, CA, Althoff, RR, Artiges, E, Banaschewski, T, Barker, GJ, Bokde, ALW, Büchel, C, Carvalho, FM, Conrod, PJ, Flor, H, Fauth-Bühler, M, Frouin, V, Gallinat, J, Gan, G, Gowland, P, Heinz, A, Ittermann, B, Lawrence, C, Mann, K, Martinot, J-L, Nees, F, Ortiz, N, Paillère-Martinot, M-L, Paus, T, Pausova, Z, Rietschel, M, Robbins, TW, Smolka, MN, Ströhle, A, Schumann, G, Garavan, H and the IMAGEN Consortium (2014) Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512, 185.Google Scholar
Winterburn, JL, Voineskos, AN, Devenyi, GA, Plitman, E, de la Fuente-Sandoval, C, Bhagwat, N, Graff-Guerrero, A, Knight, J and Chakravarty, MM (2017) Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophrenia Research. ePub ahead of print.Google Scholar
Wolpert, DH and Macready, WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82.Google Scholar
Wong, HK, Tiffin, PA, Chappell, MJ, Nichols, TE, Welsh, PR, Doyle, OM, Lopez-Kolkovska, BC, Inglis, SK, Coghill, D, Shen, Y and Tiño, P (2017) Personalized medication response prediction for attention-deficit hyperactivity disorder: learning in the model space vs. learning in the data space. Frontiers in Physiology 8, 199.Google Scholar
World Health Organization (2014) Mental Health: A State of Well-Being. Available at https://www.who.int/features/factfiles/mental_health/en/ (accessed 30 January 2019).Google Scholar
Wu, J-L, Yu, L-C and Chang, P-C (2012) Detecting causality from online psychiatric texts using inter-sentential language patterns. BMC Medical Informatics and Decision Making 12, 72.Google Scholar
Wu, M-J, Mwangi, B, Passos, IC, Bauer, IE, Cao, B, Frazier, TW, Zunta-Soares, GB and Soares, JC (2017) Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study. International Journal of Bipolar Disorders 5, 32.Google Scholar
Xiao, X, Fang, H, Wu, J, Xiao, C, Xiao, T, Qian, L, Liang, F, Xiao, Z, Chu, KK and Ke, X (2017) Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Research 10, 620–630.Google Scholar
Xu, R and Zhang, Q (2016) Social dynamics of the online health communities for mental health. In Zheng, X, Zeng, D, Chen, H and Leischow, S (eds), Smart Health. ICSH 2015. Lecture Notes in Computer Science, vol 9545. Cham: Springer, pp. 267–277.Google Scholar
Xu, X, Zhu, T, Zhang, R, Li, L, Li, A, Kang, W, Fang, Z, Ning, Y and Wang, Y (2011) Pervasive mental health self-help based on cognitive-behavior therapy and machine learning. In 6th International Conference on Pervasive Computing and Applications (ICPCA), pp. 212–219.Google Scholar
Xue, Y, Li, Q, Jin, L, Feng, L, Clifton, DA and Clifford, GD (2014) Detecting adolescent psychological pressures from micro-blog. In Zhang, Y, Yao, G, He, J, Wang, L, Smalheiser, NR and Yin, X (eds), Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Cham: Springer, pp. 83–94.Google Scholar
Yaghoobi Karimu, R and Azadi, S (2018) Diagnosing the ADHD using a mixture of expert fuzzy models. International Journal of Fuzzy Systems 20, 1282–1296.Google Scholar
Yahata, N, Morimoto, J, Hashimoto, R, Lisi, G, Shibata, K, Kawakubo, Y, Kuwabara, H, Kuroda, M, Yamada, T, Megumi, F, Imamizu, H, Náñez, JE Sr, Takahashi, H, Okamoto, Y, Kasai, K, Kato, N, Sasaki, Y, Watanabe, T and Kawato, M (2016) A small number of abnormal brain connections predicts adult autism spectrum disorder. Nature Communications 7, 11254.Google Scholar
Yang, S, Zhou, P, Duan, K, Hossain, MS and Alhamid, MF (2017) Emhealth: towards emotion health through depression prediction and intelligent health recommender system. Mobile Networks and Applications 23, 216–226.Google Scholar
Yazdavar, AH, Al-Olimat, HS, Ebrahimi, M, Bajaj, G, Banerjee, T, Thirunarayan, K, Pathak, J and Sheth, A (2017) Semi-supervised approach to monitoring clinical depressive symptoms in social media. Proceedings of the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining 2017, 1191–1198.Google Scholar
Ye, Z, Rae, CL, Nombela, C, Ham, T, Rittman, T, Jones, PS, Rodríguez, PV, Coyle-Gilchrist, I, Regenthal, R, Altena, E, Housden, CR, Maxwell, H, Sahakian, BJ, Barker, RA, Robbins, TW and Rowe, JB (2016) Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures. Wiley Online Library Human Brain Mapping 37, 1026–1037.Google Scholar
Yong, Y, Yang, Y, Cui, Y, Xu, K, Liu, B, Song, M, Chen, J, Wang, H, Chen, Y, Guo, H, Li, P, Lu, L, Lv, L, Wan, P, Wang, H, Yan, H, Yan, J, Zhang, H, Zhang, D and Jiang, T (2017) Distributed functional connectivity impairment in schizophrenia: a multi-site study. In 2nd IET International Conference on Biomedical Image and Signal Processing (ICBISP 2017), Wuhan, China.Google Scholar
Yu, Y, Shen, H, Zhang, H, Zeng, L-L, Xue, Z and Hu, D (2013) Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings. Biomedical Engineering Online 12, 10.Google Scholar
Yuan, J, Holtz, C, Smith, T and Luo, J (2017) Autism spectrum disorder detection from semi-structured and unstructured medical data. EURASIP Journal on Bioinformatics & Systems Biology 2017, 3.Google Scholar
Zhang, X, Hu, B, Zhou, L, Moore, P and Chen, J (2013) An EEG based pervasive depression detection for females. In Zu, Q, Hu, B and Elçi, A (eds), Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Berlin, Heidelberg: Springer, pp. 848–861.Google Scholar
Zhang, J, Xiong, H, Huang, Y, Wu, H, Leach, K and Barnes, LE (2015 a) M-SEQ: early detection of anxiety and depression via temporal orders of diagnoses in electronic health data. In 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, pp. 2569–2577.Google Scholar
Zhang, L, Huang, X, Liu, T, Li, A, Chen, Z and Zhu, T (2015 b) Using linguistic features to estimate suicide probability of Chinese microblog users. In Zu, Q, Hu, B, Gu, N and Seng, S (eds), Human Centered Computing. HCC 2014. Lecture Notes in Computer Science, vol 8944. Cham: Springer, pp. 549–559.Google Scholar
Zhang, OR, Zhang, Y, Xu, J, Roberts, K, Zhang, XY and Xu, H (2017 a) Interweaving domain knowledge and unsupervised learning for psychiatric stressor extraction from clinical notes. In Benferhat, S, Tabia, K and Ali, M (eds), Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol 10351. Cham: Springer, pp. 396–406.Google Scholar
Zhang, Y, Zhang, O, Wu, Y, Lee, H-J, Xu, J, Xu, H and Roberts, K (2017 b) Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge. Journal of Biomedical Informatics 75S, S129–S137.Google Scholar
Zhao, W, Liu, L, Zheng, F, Fan, D, Chen, X, Yang, Y and Cai, Q (2011) Investigation into stress of mothers with mental retardation children based on EEG (Electroencephalography) and psychology instruments. In Hu, B, Liu, J, Chen, L and Zhong, N (eds), Brain Informatics. BI 2011. Lecture Notes in Computer Science, vol 6889. Berlin, Heidelberg: Springer, pp. 238–249.Google Scholar
Zhao, J, Su, W, Jia, J, Zhang, C and Lu, T (2017 a) Research on depression detection algorithm combine acoustic rhythm with sparse face recognition. Cluster Computing, pp. 1–12.Google Scholar
Zhao, S, Zhao, Q, Zhang, X, Peng, H, Yao, Z, Shen, J, Yao, Y, Jiang, H and Hu, B (2017 b) Wearable EEG-based real-time system for depression monitoring. In Zeng, Y et al. (eds), Brain Informatics. BI 2017. Lecture Notes in Computer Science, vol 10654. Cham: Springer, pp. 190–201.Google Scholar
Zhou, D, Luo, J, Silenzio, V, Zhou, Y, Hu, J and Currier, G (2015) Tackling mental health by integrating unobtrusive multimodal sensing. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015). AAAI Press, pp. 1401–1408.Google Scholar
Zhu, X (2010) Semi-Supervised learning. In Sammut, C and Webb, G (eds), Encyclopedia of Machine Learning. Boston, MA: Springer US, pp. 892–897.Google Scholar
Zhu, X and Goldberg, AB (2009) Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 3, 1–130.Google Scholar
Zhu, CZ, Zang, YF, Liang, M, Tian, LX, He, Y, Li, XB, Sui, MQ, Wang, YF and Jiang, TZ (2005) Discriminative analysis of brain function at resting-state for attention-deficit/hyperactivity disorder. Medical Image Computing and Computer-Assisted Intervention 8, 468–475.Google Scholar
Zhu, F, Panwar, B, Dodge, HH, Li, H, Hampstead, BM, Albin, RL, Paulson, HL and Guan, Y (2016) COMPASS: a computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease. Scientific Reports 6, 34567.Google Scholar
Zhu, D, Riedel, BC, Jahanshad, N, Groenewold, NA, Stein, DJ, Gotlib, IH, Sacchet, MD, Dima, D, Cole, JH, Fu, CHY, Walter, H, Veer, IM, Frodl, T, Schmaal, L, Veltman, DJ and Thompson, PM (2017) Classification of major depressive disorder via multi-site weighted LASSO model. In Medical Image Computing and Computer-Assisted Intervention –MICCAI 2017. Springer International Publishing, pp. 159–167.Google Scholar
Zou, L, Zheng, J, Miao, C, Mckeown, MJ and Wang, ZJ (2017) 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access 5, 23626–23636.Google Scholar