Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Abi-Dargham A, Horga G. 2016. The search for imaging biomarkers in psychiatric disorders. Nat. Med. 22:1248–55 [Google Scholar]
  2. Alvarez-Jimenez M, Parker AG, Hetrick SE, McGorry PD, Gleeson JF. 2011. Preventing the second episode: a systematic review and meta-analysis of psychosocial and pharmacological trials in first-episode psychosis. Schizophr. Bull. 37:619–30 [Google Scholar]
  3. Amur S, LaVange L, Zineh I, Buckman-Garner S, Woodcock J. 2015. Biomarker qualification: toward a multiple stakeholder framework for biomarker development, regulatory acceptance, and utilization. Clin. Pharmacol. Ther. 98:34–46 [Google Scholar]
  4. Arbabshirani MR, Plis S, Sui J, Calhoun VD. 2017. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145:137–65 [Google Scholar]
  5. Arnedo J, Svrakic DM, del Val C, Romero-Zaliz R, Hernandez-Cuervo H. et al. 2015. Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies. Am. J. Psychiatry 172:139–53 [Google Scholar]
  6. Askland KD, Garnaat S, Sibrava NJ, Boisseau CL, Strong D. et al. 2015. Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy. Int. J. Methods Psychiatr. Res. 24:156–69 [Google Scholar]
  7. Ball TM, Stein MB, Ramsawh HJ, Campbell-Sills L, Paulus MP. 2014. Single-subject anxiety treatment outcome prediction using functional neuroimaging. Neuropsychopharmacology 39:1254–61 [Google Scholar]
  8. Bastani O, Kim C, Bastani H. 2017. Interpretability via model extraction. arXiv:1706.09773 [cs.LG]
  9. Begley CG, Ellis LM. 2012. Drug development: raise standards for preclinical cancer research. Nature 483:531–33 [Google Scholar]
  10. Bertocci MA, Bebko G, Versace A, Iyengar S, Bonar L. et al. 2017. Reward-related neural activity and structure predict future substance use in dysregulated youth. Psychol. Med. 47:1357–69 [Google Scholar]
  11. Bishop CM. 2006. Pattern Recognition and Machine Learning New York: Springer [Google Scholar]
  12. Bohannon J. 2015. The synthetic therapist. Science 349:624525051 [Google Scholar]
  13. Borsboom D, Cramer AO. 2013. Network analysis: an integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 9:91–121 [Google Scholar]
  14. Breiman L, Spector P. 1992. Submodel selection and evaluation in regression: the X-random case. Int. Stat. Rev. 60:291–319 [Google Scholar]
  15. Brown MR, Sidhu GS, Greiner R, Asgarian N, Bastani M. et al. 2012. ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Front. Syst. Neurosci. 6:69 [Google Scholar]
  16. Bzdok D, Varoquaux G, Thirion B. 2016. Neuroimaging research: from null-hypothesis falsification to out-of-sample generalization. Educ. Psychol. Meas. 77:868–80 [Google Scholar]
  17. Bzdok D, Yeo BTT. 2017. Inference in the age of big data: future perspectives on neuroscience. NeuroImage 155:549–64 [Google Scholar]
  18. Cannon TD, Yu C, Addington J, Bearden CE, Cadenhead KS. et al. 2016. An individualized risk calculator for research in prodromal psychosis. Am. J. Psychiatry 173:980–88Prediction of psychosis using classical statistical methods highlighted as a counterpoint to using a machine learning approach for prediction in psychiatry. [Google Scholar]
  19. Carrion RE, Cornblatt BA, Burton CZ, Tso IF, Auther AM. et al. 2016. Personalized prediction of psychosis: external validation of the NAPLS-2 psychosis risk calculator with the EDIPPP project. Am. J. Psychiatry 173:989–96 [Google Scholar]
  20. Carter KW, Francis RW, Bresnahan M, Gissler M, Grønborg TK. et al. 2015. ViPAR: a software platform for the Virtual Pooling and Analysis of Research data. Int. J. Epidemiol. 45:408–16 [Google Scholar]
  21. Chekroud AM, Gueorguieva R, Krumholz HM, Trivedi MH, Krystal JH, McCarthy G. 2017. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 74:437078 [Google Scholar]
  22. Chekroud AM, Zotti RJ, Shehzad Z, Gueorguieva R, Johnson MK. et al. 2016. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 3:243–50 [Google Scholar]
  23. Chen JH, Asch SM. 2017. Machine learning and prediction in medicine: beyond the peak of inflated expectations. N. Engl. J. Med. 376:2507–9 [Google Scholar]
  24. Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Ethridge LE. et al. 2016. Identification of distinct psychosis biotypes using brain-based biomarkers. Am. J. Psychiatry 173:373–84 [Google Scholar]
  25. Cortes C, Vapnik V. 1995. Support-vector networks. Mach. Learn. 20:273–97 [Google Scholar]
  26. Csernansky JG, Schindler MK, Splinter NR, Wang L, Gado M. et al. 2004. Abnormalities of thalamic volume and shape in schizophrenia. Am. J. Psychiatry 161:896–902 [Google Scholar]
  27. Cumming G. 2014. The new statistics: why and how. Psychol. Sci. 25:7–29 [Google Scholar]
  28. Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. 2008. Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imaging. Neurobiol. Aging 29:514–23 [Google Scholar]
  29. de Wit S, Ziermans TB, Nieuwenhuis M, Schothorst PF, van Engeland H. et al. 2017. Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: applying machine learning techniques to brain imaging data. Hum. Brain Mapp. 38:704–14 [Google Scholar]
  30. Deco G, Kringelbach ML. 2014. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84:892–905 [Google Scholar]
  31. Diniz BS, Lin CW, Sibille E, Tseng G, Lotrich F. et al. 2016. Circulating biosignatures of late-life depression (LLD): towards a comprehensive, data-driven approach to understanding LLD pathophysiology. J. Psychiatr. Res. 82:1–7 [Google Scholar]
  32. Doehrmann O, Ghosh SS, Polli FE, Reynolds GO, Horn F. et al. 2013. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry 70:87–97 [Google Scholar]
  33. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F. et al. 2017. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23:28–38 [Google Scholar]
  34. Du Y, Pearlson GD, Liu J, Sui J, Yu Q. et al. 2015. A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders. NeuroImage 122:272–80 [Google Scholar]
  35. Eberhart R, Kennedy J. 1995. A new optimizer using particle swarm theory Presented at Int. Symp. Micro Mach. Hum. Sci., 6th, Nagoya, Jpn [Google Scholar]
  36. Eklund A, Andersson M, Josephson C, Johannesson M, Knutsson H. 2012. Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. NeuroImage 61:565–78 [Google Scholar]
  37. Eklund A, Nichols TE, Knutsson H. 2016. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. PNAS 113:7900–5Demonstrates the limitations of inferences made from magnetic resonance imaging studies using standard methods. [Google Scholar]
  38. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM. et al. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–18 [Google Scholar]
  39. Etkin A, Patenaude B, Song YJ, Usherwood T, Rekshan W. et al. 2015. A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology 40:1332–42 [Google Scholar]
  40. Fair DA, Bathula D, Nikolas MA, Nigg JT. 2012. Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. PNAS 109:6769–74 [Google Scholar]
  41. Fajutrao L, Locklear J, Priaulx J, Heyes A. 2009. A systematic review of the evidence of the burden of bipolar disorder in Europe. Clin. Pract. Epidemiol. Ment. Health 5:3 [Google Scholar]
  42. Filzmoser P, Liebmann B, Varmuza K. 2009. Repeated double cross validation. J. Chemometr. 23:160–71 [Google Scholar]
  43. Fisher RA. 1938. The statistical utilization of multiple measurements. Ann. Hum. Genet. 8:376–86 [Google Scholar]
  44. Fong R, Vedaldi A. 2017. Interpretable explanations of black boxes by meaningful perturbation. arXiv:1704.03296 [cs.CV]
  45. Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat. Rev. Neurosci. 16:159–72 [Google Scholar]
  46. Freedman R, Lewis DA, Michels R, Pine DS, Schultz SK. et al. 2013. The initial field trials of DSM-5: new blooms and old thorns. Am. J. Psychiatry 170:1–5 [Google Scholar]
  47. Fu CH, Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF. et al. 2008. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol. Psychiatry 63:656–62 [Google Scholar]
  48. Fusar-Poli P, Borgwardt S, Bechdolf A, Addington J, Riecher-Rossler A. et al. 2013. The psychosis high-risk state: a comprehensive state-of-the-art review. JAMA Psychiatry 70:107–20 [Google Scholar]
  49. Gabrieli JD, Ghosh SS, Whitfield-Gabrieli S. 2015. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85:11–26 [Google Scholar]
  50. Galton F. 1907. Vox populi (the wisdom of crowds). Nature 75:450–51 [Google Scholar]
  51. Goodman S. 1992. A comment on replication, p-values and evidence. Stat. Med. 11:875–79 [Google Scholar]
  52. Goodman SN, Fanelli D, Ioannidis JP. 2016. What does research reproducibility mean. ? Sci. Transl. Med. 8:341ps12 [Google Scholar]
  53. Goodsaid F, Mattes WB. 2013. The Path from Biomarker Discovery to Regulatory Qualification Cambridge, MA: Academic [Google Scholar]
  54. Guo W, Zhang K, Lin L, Huang S, Xing X. 2017. Towards interrogating discriminative machine learning models. arXiv:1705.08564 [cs.LG]
  55. Hahn T, Kircher T, Straube B, Wittchen HU, Konrad C. et al. 2015. Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry 72:68–74 [Google Scholar]
  56. Harrison G, Hopper K, Craig T, Laska E, Siegel C. et al. 2001. Recovery from psychotic illness: a 15- and 25-year international follow-up study. Br. J. Psychiatry 178:506–17 [Google Scholar]
  57. Hart A, Wyatt J. 1990. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Med. Inform. 15:229–36 [Google Scholar]
  58. Hasan A, Wobrock T, Guse B, Langguth B, Landgrebe M. et al. 2017. Structural brain changes are associated with response of negative symptoms to prefrontal repetitive transcranial magnetic stimulation in patients with schizophrenia. Mol. Psychiatry 22:857–64 [Google Scholar]
  59. Hastie T, Tibshirani R, Friedman J. 2009. The Elements of Statistical Learning New York: Springer [Google Scholar]
  60. Hegarty JD, Baldessarini RJ, Tohen M, Waternaux C, Oepen G. 1994. One hundred years of schizophrenia: a meta-analysis of the outcome literature. Am. J. Psychiatry 151:1409–16 [Google Scholar]
  61. Hirschfeld R, Lewis L, Vornik LA. 2003. Perceptions and impact of bipolar disorder: How far have we really come? Results of the National Depressive and Manic-Depressive Association 2000 survey of individuals with bipolar disorder. J. Clin. Psychiatry 64:161–74 [Google Scholar]
  62. Hoexter MQ, Miguel EC, Diniz JB, Shavitt RG, Busatto GF, Sato JR. 2013. Predicting obsessive-compulsive disorder severity combining neuroimaging and machine learning methods. J. Affect. Disord. 150:1213–16 [Google Scholar]
  63. Hofmann SG, Asnaani A, Vonk IJ, Sawyer AT, Fang A. 2012. The efficacy of cognitive behavioral therapy: a review of meta-analyses. Cogn. Ther. Res. 36:427–40 [Google Scholar]
  64. Hotelling H. 1933. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24:417–41 [Google Scholar]
  65. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS. et al. 2010. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167:748–51 [Google Scholar]
  66. Insel TR, Cuthbert BN. 2015. Brain disorders? Precisely. Science 348:499–500 [Google Scholar]
  67. Ioannidis JP. 2005. Why most published research findings are false. PLOS Med 2:e124Influential study questioning the dominant statistical framework and research design. [Google Scholar]
  68. Ioannidis JP. 2016. Why most clinical research is not useful. PLOS Med 13:e1002049 [Google Scholar]
  69. James G, Witten D, Hastie T, Tibshirani R. 2015. An Introduction to Statistical Learning with Applications in R New York: Springer [Google Scholar]
  70. Jha S, Topol EJ. 2016. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316:2353–54 [Google Scholar]
  71. Jordan MI, Mitchell TM. 2015. Machine learning: trends, perspectives, and prospects. Science 349:255–60 [Google Scholar]
  72. Kambeitz J, Cabral C, Sacchet MD, Gotlib IH, Zahn R. et al. 2016. Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies. Biol. Psychiatry 82:330–38 [Google Scholar]
  73. Kambeitz J, Kambeitz-Ilankovic L, Leucht S, Wood S, Davatzikos C. et al. 2015. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology 40:1742–51Meta-analysis of machine learning studies conducted in schizophrenia demonstrating their potential as biomarkers. [Google Scholar]
  74. Kapur S, Phillips AG, Insel TR. 2012. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?. Mol. Psychiatry 17:1174–79 [Google Scholar]
  75. Katuwal GJ, Chen R. 2016. Machine learning model interpretability for precision medicine. arXiv:1610.09045 [q-bio.QM]
  76. Kessler RC, van Loo HM, Wardenaar KJ, Bossarte RM, Brenner LA. et al. 2016. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol. Psychiatry 21:1366–71 [Google Scholar]
  77. Khodayari-Rostamabad A, Hasey GM, Maccrimmon DJ, Reilly JP, de Bruin H. 2010. A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clin. Neurophysiol. 121:1998–2006 [Google Scholar]
  78. Khodayari-Rostamabad A, Reilly JP, Hasey GM, de Bruin H, Maccrimmon DJ. 2013. A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clin. Neurophysiol. 124:1975–85 [Google Scholar]
  79. Kloppel S, Stonnington CM, Barnes J, Chen F, Chu C. et al. 2008. Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method. Brain 131:2969–74 [Google Scholar]
  80. Koh PW, Liang P. 2017. Understanding black-box predictions via influence functions. arXiv:1703.04730 [stat.ML]
  81. Konig IR, Malley JD, Weimar C, Diener HC, Ziegler A. 2007. Practical experiences on the necessity of external validation. Stat. Med. 26:5499–511 [Google Scholar]
  82. Koutsouleris N, Borgwardt S, Meisenzahl EM, Bottlender R, Moller HJ, Riecher-Rossler A. 2012. Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study. Schizophr. Bull. 38:1234–46 [Google Scholar]
  83. Koutsouleris N, Davatzikos C, Borgwardt S, Gaser C, Bottlender R. et al. 2014. Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr. Bull. 40:1140–53 [Google Scholar]
  84. Koutsouleris N, Kahn RS, Chekroud AM, Leucht S, Falkai P. et al. 2016. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry 3:935–46Demonstrates the assessment of multiple forms of generalizability using a leave-site-out methodology. [Google Scholar]
  85. Koutsouleris N, Meisenzahl E, Borgwardt S, Riecher-Rossler A, Frodl T. et al. 2015. Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers. Brain 138:2059–73 [Google Scholar]
  86. Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T. et al. 2009. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch. Gen. Psychiatry 66:700–12First study to show the potential of machine learning techniques in neuroimaging to predict the onset of psychosis. [Google Scholar]
  87. Lavagnino L, Amianto F, Mwangi B, D'Agata F, Spalatro A. et al. 2015. Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach. Psychol. Med. 45:2805–12 [Google Scholar]
  88. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44 [Google Scholar]
  89. Lessov-Schlaggar CN, Rubin JB, Schlaggar BL. 2016. The fallacy of univariate solutions to complex systems problems. Front. Neurosci. 10:267 [Google Scholar]
  90. Lisboa PJ. 2002. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15:11–39 [Google Scholar]
  91. Long E, Lin H, Liu Z, Wu X, Wang L. et al. 2017. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat. Biomed. Eng. 1:0024 [Google Scholar]
  92. Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K. et al. 2015. Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J. Affect. Disord. 184:182–92 [Google Scholar]
  93. Marquand AF, Rezek I, Buitelaar J, Beckmann CF. 2016. Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol. Psychiatry 80:552–61 [Google Scholar]
  94. Mechelli A, Lin A, Wood S, McGorry P, Amminger P. et al. 2017. Using clinical information to make individualized prognostic predictions in people at ultra high risk for psychosis. Schizophr. Res. 184:32–38 [Google Scholar]
  95. Miller KL, Alfaro-Almagro F, Bangerter NK, Thomas DL, Yacoub E. et al. 2016. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19:1523–36 [Google Scholar]
  96. Miotto R, Li L, Kidd BA, Dudley JT. 2016. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6:26094 [Google Scholar]
  97. Modai I, Israel A, Mendel S, Hines EL, Weizman R. 1996. Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in-patients. J. Med. Syst. 20:403–12 [Google Scholar]
  98. Modai I, Kurs R, Ritsner M, Oklander S, Silver H. et al. 2002. Neural network identification of high-risk suicide patients. Med. Inform. Internet Med. 27:39–47 [Google Scholar]
  99. Modai I, Stoler M, Inbarsaban N, Saban N. 1993. Clinical decisions for psychiatric inpatients and their evaluation by a trained neural-network. Methods Inf. Med. 32:396–99 [Google Scholar]
  100. Mohr DC, Zhang M, Schueller SM. 2017. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu. Rev. Clin. Psychol. 13:23–47 [Google Scholar]
  101. Molenaar PC, Campbell CG. 2009. The new person-specific paradigm in psychology. Curr. Dir. Psychol. Sci. 18:112–17Questions the classical psychological paradigm of latent variables and highlights the importance of individual differences. [Google Scholar]
  102. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR. et al. 2005. Ways toward an early diagnosis in Alzheimer's disease: the Alzheimer's Disease Neuroimaging Initiative (ADNI). Alzheimers Dement 1:55–66 [Google Scholar]
  103. Nuzzo R. 2014. Scientific method: statistical errors. Nature 506:150–52 [Google Scholar]
  104. O'Neil C. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy New York: Crown [Google Scholar]
  105. Opel N, Redlich R, Kaehler C, Grotegerd D, Dohm K. et al. 2017. Prefrontal gray matter volume mediates genetic risks for obesity. Mol. Psychiatry 22:703–10 [Google Scholar]
  106. Orru G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. 2012. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36:1140–52 [Google Scholar]
  107. Perlis RH. 2013. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol. Psychiatry 74:7–14 [Google Scholar]
  108. Perry PJ, Bever KA, Arndt S, Combs MD. 1998. Relationship between patient variables and plasma clozapine concentrations: a dosing nomogram. Biol. Psychiatry 44:733–38 [Google Scholar]
  109. Pettersson-Yeo W, Benetti S, Marquand A, Dell'Acqua F, Williams S. et al. 2013. Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychol. Med. 43:2547–62 [Google Scholar]
  110. Polikar R. 2006. Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6:21–45 [Google Scholar]
  111. Ramyead A, Studerus E, Kometer M, Uttinger M, Gschwandtner U. et al. 2016. Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naive at-risk patients. World J. Biol. Psychiatry 17:285–95 [Google Scholar]
  112. Redlich R, Almeida JR, Grotegerd D, Opel N, Kugel H. et al. 2014. Brain morphometric biomarkers distinguishing unipolar and bipolar depression: a voxel-based morphometry–pattern classification approach. JAMA Psychiatry 71:1222–30 [Google Scholar]
  113. Rosenbaum L. 2015. Transitional chaos or enduring harm? The EHR and the disruption of medicine. New Engl. J. Med. 373:1585–88 [Google Scholar]
  114. Rosenblatt F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65:386–408 [Google Scholar]
  115. Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Stewart JW. et al. 2006. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR* D report. Am. J. Psychiatry 163:1905–17 [Google Scholar]
  116. Sawilowsky SS. 2009. New effect size rules of thumb. J. Mod. Appl. Stat. Methods 8:597–99 [Google Scholar]
  117. Schizophr. Work. Group. 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511421–27 [Google Scholar]
  118. Schmaal L, Marquand AF, Rhebergen D, van Tol MJ, Ruhe HG. et al. 2015. Predicting the naturalistic course of major depressive disorder using clinical and multimodal neuroimaging information: a multivariate pattern recognition study. Biol. Psychiatry 78:278–86 [Google Scholar]
  119. Schnack HG, Kahn RS. 2016. Detecting neuroimaging biomarkers for psychiatric disorders: sample size matters. Front. Psychiatry 7:50 [Google Scholar]
  120. Schooler JW. 2014. Metascience could rescue the “replication crisis. .” Nature 515:9 [Google Scholar]
  121. Schumann G, Binder EB, Holte A, de Kloet ER, Oedegaard KJ. et al. 2014. Stratified medicine for mental disorders. Eur. Neuropsychopharmacol. 24:5–50 [Google Scholar]
  122. Setoyama D, Kato TA, Hashimoto R, Kunugi H, Hattori K. et al. 2016. Plasma metabolites predict severity of depression and suicidal ideation in psychiatric patients: a multicenter pilot analysis. PLOS ONE 11:e0165267 [Google Scholar]
  123. Silva RF, Castro E, Gupta CN, Cetin M, Arbabshirani M. et al. 2014. The tenth annual MLSP competition: schizophrenia classification challenge. 2014 IEEE International Workshop on Machine Learning for Signal Processing M Mboup, T Adali, E Moreau, J Larsen New York: IEEE https://doi.org/10.1109/MLSP.2014.6958889 [Crossref] [Google Scholar]
  124. Siskind D, McCartney L, Goldschlager R, Kisely S. 2016. Clozapine v. first- and second-generation antipsychotics in treatment-refractory schizophrenia: systematic review and meta-analysis. Br. J. Psychiatry 209:385–92 [Google Scholar]
  125. Snoek J, Larochelle H, Adams RP. 2012. Practical Bayesian optimization of machine learning algorithms. arXiv:1206.2944 [stat.ML]
  126. Stone M. 1974. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Stat. Methodol. 36:111–47 [Google Scholar]
  127. Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA. et al. 2014. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav 8:153–82 [Google Scholar]
  128. Tognin S, Pettersson-Yeo W, Valli I, Hutton C, Woolley J. et al. 2014. Using structural neuroimaging to make quantitative predictions of symptom progression in individuals at ultra-high risk for psychosis. Front. Psychiatry 4:187 [Google Scholar]
  129. Torgalsboen AK, Rund BR. 2002. Lessons learned from three studies of recovery from schizophrenia. Int. Rev. Psychiatry 14:312–17 [Google Scholar]
  130. Tran T, Luo W, Phung D, Harvey R, Berk M. et al. 2014. Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments. BMC Psychiatry 14:76 [Google Scholar]
  131. Tulio Ribeiro M, Singh S, Guestrin C. 2016.. “ Why should I trust you?” Explaining the predictions of any classifier. arXiv:1602.04938 [cs.LG] Early study providing techniques to open the black box of machine learning.
  132. van de Leemput IA, Wichers M, Cramer AO, Borsboom D, Tuerlinckx F. et al. 2014. Critical slowing down as early warning for the onset and termination of depression. PNAS 111:87–92 [Google Scholar]
  133. van der Gaag M, Smit F, Bechdolf A, French P, Linszen DH. et al. 2013. Preventing a first episode of psychosis: meta-analysis of randomized controlled prevention trials of 12 month and longer-term follow-ups. Schizophr. Res. 149:56–62 [Google Scholar]
  134. Van Der Maaten L, Postma E, Van den Herik J. 2009. Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10:66–71 [Google Scholar]
  135. Van Schependom J, Yu WP, Gielen J, Laton J, De Keyser J. et al. 2015. Do advanced statistical techniques really help in the diagnosis of the metabolic syndrome in patients treated with second-generation antipsychotics?. J. Clin. Psychiatry 76:E1292–99 [Google Scholar]
  136. Varma S, Simon R. 2006. Bias in error estimation when using cross-validation for model selection. BMC Bioinformat 7:91 [Google Scholar]
  137. Varol E, Sotiras A, Davatzikos C. 2016. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage 145:346–64 [Google Scholar]
  138. Varoquaux G, Raamana PR, Engemann DA, Hoyos-Idrobo A, Schwartz Y, Thirion B. 2017. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage 145:166–79 [Google Scholar]
  139. Vickers AJ, Elkin EB. 2006. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Making 26:565–74 [Google Scholar]
  140. Visser RM, Haver P, Zwitser RJ, Scholte HS, Kindt M. 2016. First steps in using multi-voxel pattern analysis to disentangle neural processes underlying generalization of spider fear. Front. Hum. Neurosci. 10:222 [Google Scholar]
  141. Wachter R. 2015. The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age New York: McGraw-Hill [Google Scholar]
  142. Wang L, Alpert KI, Calhoun VD, Cobia DJ, Keator DB. et al. 2016. SchizConnect: mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. NeuroImage 124:1155–67 [Google Scholar]
  143. West JB. 2005. The physiological challenges of the 1952 Copenhagen poliomyelitis epidemic and a renaissance in clinical respiratory physiology. J. Appl. Physiol. 99:424–32 [Google Scholar]
  144. Whelan R, Garavan H. 2014. When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol. Psychiatry 75:746–48 [Google Scholar]
  145. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E. et al. 2014. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature 512:185–89 [Google Scholar]
  146. Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A, Saygin Z, Doehrmann O. et al. 2016. Brain connectomics predict response to treatment in social anxiety disorder. Mol. Psychiatry 21:680–85 [Google Scholar]
  147. Widmer G, Kubat M. 1996. Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23:69–101 [Google Scholar]
  148. Wolpert DH. 1992. Stacked generalization. Neural Netw 5:241–59 [Google Scholar]
  149. Wong EHF, Yocca F, Smith MA, Lee CM. 2010. Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters' perspective. Int. J. Neuropsychopharmacol. 13:1269–84 [Google Scholar]
  150. Woo CW, Chang LJ, Lindquist MA, Wager TD. 2017. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20:365–77Excellent review of the use of machine learning for translational neuroimaging. [Google Scholar]
  151. Woodcock J, Buckman S, Goodsaid F, Walton MK, Zineh I. 2011. Qualifying biomarkers for use in drug development: a US Food and Drug Administration overview. Expert Opin. Med. Diagn. 5:369–74 [Google Scholar]
  152. Wu MJ, Mwangi B, Bauer IE, Passos IC, Sanches M. et al. 2016. Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning. NeuroImage 145:254–64 [Google Scholar]
  153. Wunderink L, Sytema S, Nienhuis FJ, Wiersma D. 2009. Clinical recovery in first-episode psychosis. Schizophr. Bull. 35:362–69 [Google Scholar]
  154. Yang C, Delcher C, Shenkman E, Ranka S. 2016. Predicting 30-day all-cause readmissions from hospital inpatient discharge data Presented at IEEE Int. Conf. E-Health Netw. Appl. Serv., 18th, Sept. 14–17, Munich, Ger [Google Scholar]
  155. Yu KH, Zhang C, Berry GJ, Altman RB, Re C. et al. 2016. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7:12474 [Google Scholar]
  156. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Stat. Methodol. 67:301–20 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error