Всемирная психиатрия. №2 2021

Перспективы машинного обучения в прогнозировании исходов лечения в психиатрии

Adam M. Chekroud1,2, Julia Bondar2 , Jaime Delgadillo3 , Gavin Doherty4 , Akash Wasil5 , Marjolein Fokkema6 , Zachary Cohen7 , Danielle Belgrave8 , Robert DeRubeis5 , Raquel Iniesta9 , Dominic Dwyer10, Karmel Choi11,12

1 Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA; 2 Spring Health, New York City, NY, USA; 3 Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK; 4 School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland; 5 Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA; 6 Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands; 7 Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA; 8 Microsoft Research, Cambridge, UK; 9 Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, UK; 10Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig Maximilian University, Munich, Germany; 11Harvard T.H. Chan School of Public Health, Boston, MA, USA; 12Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Стр. 154–170

648 просмотров

Опубликовано: 1 июня 2021

Аннотация / Abstract

a:2:{s:4:"TEXT";s:65534:"a:2:{s:4:"TYPE";s:4:"HTML";s:4:"TEXT";s:110742:"Лечебные вмешательства в психиатрии эффективны далеко не во всех случаях, когда они показаны. Например, при депрессии только 30–50% людей достигают ремиссии после любой стартовой терапии, даже в контексте хорошо проведенного клинического исследования 1 . В конце концов, после нескольких попыток или комбинации лечения, большинство пациентов все же достигают ремиссии. Однако что, если вместо того, чтобы перебирать доступные методы лечения, которые потенциально могут принести пользу пациенту, мы могли бы с самого начала спрогнозировать правильное лечение для каждого конкретного человека?Исследователи десятилетиями старались достичь этого. Изначально ученые пытались выявить конкретные факторы, определяющие ответ на лечение на основе теоретических обоснований, что привело к появлению исследований, сосредоточенных на отдельных переменных: стресс в раннем детстве, суицидальность, важные жизненные события или сопутствующие диагнозы. С тех пор продолжающийся поиск одной (или нескольких) истинных переменных проводился на разных уровнях: пациента (клинические характеристики, маркеры крови), его/ее мозга (структурная и функциональная нейровизуализация, церебральный кровоток, биоэлектрическая активность), его/ее генов (однонуклеотидные полиморфизмы, мутации/редкие генетические варианты, вариации числа копий, экспрессия генов) и характеристик вмешательств (выбранный препарат или метод психотерапии, способ его доставки, терапевтический альянс). Если бы одна переменная могла точно предсказать реакцию на лечение, наши специалисты, вероятно, уже нашли бы ее. Вместо этого большинство выявленных переменных продемонстрировали небольшую объяснительную силу в отношении результатов лечения, и внимание исследователей, естественно, переключилось на многомерные модели, способные объединить множество меньших эффектов.Машинное обучение – это набор статистических подходов и инструментов, которые очень хорошо по...

Лечебные вмешательства в психиатрии эффективны далеко не во всех случаях, когда они показаны. Например, при депрессии только 30–50% людей достигают ремиссии после любой стартовой терапии, даже в контексте хорошо проведенного клинического исследования 1 . В конце концов, после нескольких попыток или комбинации лечения, большинство пациентов все же достигают ремиссии. Однако что, если вместо того, чтобы перебирать доступные методы лечения, которые потенциально могут принести пользу пациенту, мы могли бы с самого начала спрогнозировать правильное лечение для каждого конкретного человека?Исследователи десятилетиями старались достичь этого. Изначально ученые пытались выявить конкретные факторы, определяющие ответ на лечение на основе теоретических обоснований, что привело к появлению исследований, со�...

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1. Rush AJ, Trivedi MH, Wisniewski SR et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 2006;163:1905-17.
2. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York: Springer, 2009.
3. Chekroud AM. Bigger data, harder questions – opportunities throughout mental health care. JAMA Psychiatry 2017;64:44-50.
4. Iniesta R, Stahl D, McGuffin P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med 2016;46:2455-65.
5. Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 2001;16:199-231.
6. Delgadillo J. Machine learning: a primer for psychotherapy researchers. Psychother Res 2021;31:1-4.
7. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol 2019;19:64.
8. Cohen ZD, DeRubeis RJ. Treatment selection in depression. Annu Rev Clin Psychol 2018;14:209-36.
9. Kessler RC, van Loo HM, Wardenaar KJ et al. Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder. Epidemiol Psychiatr Sci 2017;26:22-36.
10. Maj M, Stein DJ, Parker G et al. The clinical characterization of the adult patient with depression aimed at personalization of management. World Psychiatry 2020;19:269-93.
11. Perlis RH. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry 2013;74:7-14.
12. Gravesteijn BY, Nieboer D, Ercole A et al. Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J Clin Epidemiol 2020;122:95-107.
13. Nusinovici S, Tham YC, Yan MYC et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J Clin Epidemiol 2020;122:56-69.
14. Christodoulou E, Ma J, Collins GS et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol 2019;110:12-22.
15. Desai RJ, Wang SV, Vaduganathan M et al. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open 2020;3:e1918962.
16. Lynam AL, Dennis JM, Owen KR et al. Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults. Diagn Progn Res 2020;4:6.
17. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1-22.
18. Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Meth 2009;14:323.
19. Bühlmann P. Bagging, boosting and ensemble methods. In: Gentle J, Härdle W, Mori Y (eds). Berlin: Springer, 2012:985-1022.
20. van der Laan MJ, Polley EC, Hubbard AE et al. Super learner. Stat Appl Genet Mol Biol 2007;6:4765-74.
21. Kessler RC, van Loo HM, Wardenaar KJ et al. Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry 2016;21:1366-71.
22. Pearson R, Pisner D, Meyer B et al. A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression. Psychol Med 2019;49:2330-41.
23. Webb CA, Trivedi MH, Cohen ZD et al. Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study. Psychol Med 2019;49:1118-27.
24. Hilbert K, Kunas SL, Lueken U et al. Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: a machine learning approach. Behav Res Ther 2020;124:103530.
25. Koutsouleris N, Kahn RS, Chekroud AM et al. Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach. Lancet Psychiatry 2016;3:935-46.
26. Fernández-Delgado M, Cernadas E, Barro S et al. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 2014;15:3133-81.
27. Gacto MJ, Soto-Hidalgo JM, Alcalá-Fdez J et al. Experimental study on 164 algorithms available in software tools for solving standard non-linear regression problems. IEEE Access 2019;7:108916-39.
28. Wainer J. Comparison of 14 different families of classification algorithms on 115 binary datasets. arXiv 2016;1606.00930.
29. Zhang C, Liu C, Zhang X et al. An up-to-date comparison of state-of-the-art classification algorithms. Expert Syst Appl 2017;82:128-50.
30. Hand DJ. Classifier technology and the illusion of progress. Stat Sci 2006;21:1-14.
31. Holte RC. Very simple classification rules perform well on most commonly used datasets. Mach Learn 1993;11:63-90.
32. Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci 2017;12:1100-22.
33. de Rooij M, Pratiwi BC, Fokkema M et al. The early roots of statistical learning in the psychometric literature: a review and two new results. arXiv 2018;1911.11463.
34. Larson SC. The shrinkage of the coefficient of multiple correlation. J Educ Psychol 1931;22:45-55.
35. Mosier CI. I. Problems and designs of cross-validation 1. Educ Psychol Meas 1951;11:5-11.
36. Wainer H. Estimating coefficients in linear models: it don't make no nevermind. Psychol Bull 1976;83:213-7.
37. Delgadillo J, Lutz W. A development pathway towards precision mental health care. JAMA Psychiatry 2020;77:889-90.
38. Browning M, Carter CS, Chatham C et al. Realizing the clinical potential of computational psychiatry: report from the Banbury Center meeting, February 2019. Biol Psychiatry 2020;88:e5-10.
39. Chekroud AM, Koutsouleris N. The perilous path from publication to practice. Mol Psychiatry 2018;23:24-5.
40. Chekroud AM, Zotti RJ, Shehzad Z et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry 2016;3:243-50.
41. Nie Z, Vairavan S, Narayan VA et al. Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study. PLoS One 2018;13:e0197268.
42. Iniesta R, Malki K, Maier W et al. Combining clinical variables to optimize prediction of antidepressant treatment outcomes. J Psychiatr Res 2016;78:94-102.
43. Iniesta R, Hodgson K, Stahl D et al. Antidepressant drug-specific prediction of depression treatment outcomes from genetic and clinical variables. Sci Rep 2018;8:5530.
44. Drysdale AT, Grosenick L, Downar J et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23:28-38.
45. Dinga R, Schmaal L, Penninx B et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of. Neuroimage Clin 2019;22:101796.
46. Chekroud AM, Gueorguieva R, Krumholz HM et al. Reevaluating the efficacy and predictability of antidepressant treatments: a symptom clustering approach. JAMA Psychiatry 2017;74:370-8.
47. Bondar J, Caye A, Chekroud AM et al. Symptom clusters in adolescent depression and differential response to treatment: a secondary analysis of the Treatment for Adolescents with Depression Study randomised trial. Lancet Psychiatry 2020;7:337-43.
48. Gueorguieva R, Chekroud AM, Krystal JH. Trajectories of relapse in randomised, placebo-controlled trials of treatment discontinuation in major depressive disorder: an individual patient-level data meta-analysis. Lancet Psychiatry 2017;4:230-7.
49. Paul R, Andlauer Till FM, Czamara D et al. Treatment response classes in major depressive disorder identified by model-based clustering and validated by clinical prediction models. Transl Psychiatry 2019;9:187.
50. Leighton SP, Upthegrove R, Krishnadas R et al. Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach. Lancet Digit Health 2019;1:e261-70.
51. Nunes A, Ardau R, Berghöfer A et al. Prediction of lithium response using clinical data. Acta Psychiatr Scand 2020;141:131-41.
52. Kim TT, Dufour S, Xu C et al. Predictive modeling for response to lithium and quetiapine in bipolar disorder. Bipolar Disord 2019;21:428-36.
53. Lutz W, Leach C, Barkham M et al. Predicting change for individual psychotherapy clients on the basis of their nearest neighbors. J Consult Clin Psychol 2005;73:904-13.
54. Lambert MJ, Bergin AE. Bergin and Garfield's handbook of psychotherapy and behavior change. Chichester: Wiley, 2021.
55. Deisenhofer AK, Delgadillo J, Rubel JA et al. Individual treatment selection for patients with posttraumatic stress disorder. Depress Anxiety 2018;35:541-50.
56. DeRubeis RJ, Cohen ZD, Forand NR et al. The personalized advantage index: translating research on prediction into individualized treatment recommendations. A demonstration. PLoS One 2014;9:1-8.
57. Cloitre M, Petkova E, Su Z et al. Patient characteristics as a moderator of posttraumatic stress disorder treatment outcome: combining symptom burden and strengths. BJPsych Open 2016;2:101-6.
58. Wallace ML, Frank E, Kraemer HC. A novel approach for developing and interpreting treatment moderator profiles in randomized clinical trials. JAMA Psychiatry 2013;70:1241-7.
59. Keefe JR, Wiltsey Stirman S, Cohen ZD et al. In rape trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete. Depress Anxiety 2018;35:330-8.
60. van Bronswijk SC, Bruijniks SJE, Lorenzo-Luaces L et al. Cross-trial prediction in psychotherapy: external validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression. Psychother Res 2021;31:78-91.
61. Cohen ZD, Kim TT, Van HL et al. A demonstration of a multi-method variable selection approach for treatment selection: recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression. Psychother Res 2020;30:137-50.
62. Aafjes-van Doorn K, Kamsteeg C, Bate J et al. A scoping review of machine learning in psychotherapy research. Psychother Res 2021;31:92-116.
63. Luedtke A, Sadikova E, Kessler RC. Sample size requirements for multivariate models to predict between-patient differences in best treatments of major depressive disorder. Clin Psychol Sci 2019;7:445-61.
64. Lorenzo-Luaces L, DeRubeis RJ, van Straten A et al. A prognostic index (PI) as a moderator of outcomes in the treatment of depression: a proof of concept combining multiple variables to inform risk-stratified stepped care models. J Affect Disord 2017;213:78-85.
65. Delgadillo J, Huey D, Bennett H et al. Case complexity as a guide for psychological treatment selection. J Consult Clin Psychol 2017;85:835-53.
66. Fisher AJ, Bosley HG, Fernandez KC et al. Open trial of a personalized modular treatment for mood and anxiety. Behav Res Ther 2019;116:69-79.
67. Lutz W, Rubel JA, Schwartz B et al. Towards integrating personalized feedback research into clinical practice: development of the Trier Treatment Navigator (TTN). Behav Res Ther 2019;120:103438.
68. Rubel JA, Fisher AJ, Husen K et al. Translating person-specific network models into personalized treatments: development and demonstration of the dynamic assessment treatment algorithm for individual networks (DATA-IN). Psychother Psychosom 2018;87:249-51.
69. Delgadillo J, Rubel J, Barkham M. Towards personalized allocation of patients to therapists. J Consult Clin Psychol 2020;88:799-808.
70. Atkins DC, Steyvers M, Imel ZE et al. Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implement Sci 2014;9:49.
71. Goldberg SB, Tanana M, Imel ZE et al. Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task. Psychother Res 2021;31:281-8.
72. Ewbank MP, Cummins R, Tablan V et al. Quantifying the association between psychotherapy content and clinical outcomes using deep learning. JAMA Psychiatry 2020;77:35-43.
73. Ewbank MP, Cummins R, Tablan V et al. Understanding the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy: a deep learning approach to automatic coding of session transcripts. Psychother Res 2020;31:326-38.
74. de Jong K, Conijn JM, Gallagher RAV et al. Using progress feedback to improve outcomes and reduce drop-out, treatment duration, and deterioration: a multilevel meta-analysis. Clin Psychol Rev 2021;85:102002.
75. Buckman JEJ, Cohen ZD, O'Driscoll C et al. Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches. Open Sci Framework (in press).
76. Green SA, Honeybourne E, Chalkley SR et al. A retrospective observational analysis to identify patient and treatment-related predictors of outcomes in a community mental health programme. BMJ Open 2015;5:e006103.
77. Andersson G, Titov N, Dear BF et al. Internet-delivered psychological treatments: from innovation to implementation. World Psychiatry 2019;18:20-8.
78. Andrews G, Basu A, Cuijpers P et al. Computer therapy for the anxiety and depression disorders is effective, acceptable and practical health care: an updated meta-analysis. J Anxiety Disord 2018;55:70-8.
79. Nahum-Shani I, Smith SN, Spring BJ et al. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med 2018;52:446-62.
80. Mohr DC, Zhang M, Schueller SM. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol 2017;13:23-47.
81. Lenhard F, Sauer S, Andersson E et al. Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: a machine learning approach. Int J Methods Psychiatr Res 2018;27:1-11.
82. Flygare O, Enander J, Andersson E et al. Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach. BMC Psychiatry 2020;20:1-9.
83. van Breda W, Bremer V, Becker D et al. Predicting therapy success for treatment as usual and blended treatment in the domain of depression. Internet Interv 2018;12:100-4.
84. Yardley L, Spring BJ, Riper H et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med 2016;51:833-42.
85. Morrison C, Doherty G. Analyzing engagement in a web-based intervention platform through visualizing log-data. J Med Internet Res 2014;16:e252.
86. Chien I, Enrique A, Palacios J et al. A machine learning approach to understanding patterns of engagement with internet-delivered mental health interventions. JAMA Netw Open 2020;3:e2010791.
87. Provoost S, Ruwaard J, van Breda W et al. Validating automated sentiment analysis of online cognitive behavioral therapy patient texts: an exploratory study. Front Psychol 2019;10:1-12.
88. Chikersal P, Belgrave D, Doherty G et al. Understanding client support strategies to improve clinical outcomes in an online mental health intervention. Presented at the ACM Conference on Human Factors in Computing Systems, Honolulu, April 2020.
89. Wallert J, Gustafson E, Held C et al. Predicting adherence to internet-delivered psychotherapy for symptoms of depression and anxiety after myocardial infarction: machine learning insights from the U-CARE heart randomized controlled trial. J Med Internet Res 2018;20:e10754.
90. Pinna M, Manchia M, Oppo R et al. Clinical and biological predictors of response to electroconvulsive therapy (ECT): a review. Neurosci Lett 2018;669:32-42.
91. Haq AU, Sitzmann AF, Goldman ML et al. Response of depression to electroconvulsive therapy: a meta-analysis of clinical predictors. J Clin Psychiatry 2015;76:1374-84.
92. Kar SK. Predictors of response to repetitive transcranial magnetic stimulation in depression: a review of recent updates. Clin Psychopharmacol Neurosci 2019;17:25-33.
93. Miljevic A, Bailey NW, Herring SE et al. Potential predictors of depressive relapse following repetitive transcranial magnetic stimulation: a systematic review. J Affect Disord 2019;256:317-23.
94. Nord CL. Predicting response to brain stimulation in depression: a roadmap for biomarker discovery. Curr Behav Neurosci Rep 2021;8:11-9.
95. Kedzior KK, Azorina V, Reitz S. More female patients and fewer stimuli per session are associated with the short-term antidepressant properties of repetitive transcranial magnetic stimulation (rTMS): a meta-analysis of 54 sham-controlled studies published between 1997-2013. Neuropsychiatr Dis Treat 2014; 10:727-56.
96. Yao Z, McCall WV, Essali N, et al. Precision ECT for major depressive disorder: a review of clinical factors, laboratory, and physiologic biomarkers as predictors of response and remission. Pers Med Psychiatry 2019;17-18:23-31.
97. Smoller JW. The use of electronic health records for psychiatric phenotyping and genomics. Am J Med Genet 2018;177:601-12.
98. Hayes JF, Marston L, Walters K et al. Lithium vs. valproate vs. olanzapine vs. quetiapine as maintenance monotherapy for bipolar disorder: a population-based UK cohort study using electronic health records. World Psychiatry 2016;15:53-8.
99. Wu C-S, Luedtke AR, Sadikova E et al. Development and validation of a machine learning individualized treatment rule in first-episode schizophrenia. JAMA Netw Open 2020;3:e1921660.
100. Pradier MF, McCoy TH Jr, Hughes M et al. Predicting treatment dropout after antidepressant initiation. Transl Psychiatry 2020;10:60.
101. Hughes MC, Pradier MF, Ross AS et al. Assessment of a prediction model for antidepressant treatment stability using supervised topic models. JAMA Netw Open 2020;3:e205308.
102. Pradier MF, Hughes MC, McCoy TH et al. Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation. Neuropsychopharmacology 2021;46:455- 61.
103. Huang SH, LePendu P, Iyer SV et al. Toward personalizing treatment for depression: Predicting diagnosis and severity. J Am Med Inform Assoc 2014;21:1069-75.
104. Hallgren KA, Bauer AM, Atkins DC. Digital technology and clinical decision making in depression treatment: current findings and future opportunities. Depress Anxiety 2017;34:494-501.
105. Willetts M, Hollowell S, Aslett L et al. Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants. Sci Rep 2018;8:7961.
106. Gravenhorst F, Muaremi A, Bardram J et al. Mobile phones as medical devices in mental disorder treatment: an overview. Pers Ubiquitous Comput 2015;19:335-53.
107. Saeb S, Lattie EG, Schueller SM et al. The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 2016;4:e2537.
108. Thakur SS, Roy RB. Predicting mental health using smart-phone usage and sensor data. J Ambient Intell Humaniz Comput (in press)..
109. Germine L, Reinecke K, Chaytor NS. Digital neuropsychology: challenges and opportunities at the intersection of science and software. Clin Neuropsychol 2019;33:271-86.
110. Passell E, Dillon GT, Baker JT et al. Digital cognitive assessment: results from the TestMyBrain NIMH Research Domain Criteria (RDoC) Field Test Battery Report. PsyArXiv 2019;10.31234.
111. Hoogendoorn M, Berger T, Schulz A et al. Predicting social anxiety treatment outcome based on therapeutic email conversations. IEEE J Biomed Health Inform 2017;21:1449-59.
112. Bremer V, Funk B, Riper H. Heterogeneity matters: predicting self-esteem in online interventions based on ecological momentary assessment data. Depress Res Treat 2019;2019:3481624.
113. Riihimäki H, Chachólski W, Theorell J et al. A topological data analysis based classification method for multiple measurements. BMC Bioinformatics 2020;21:336.
114. Gillan CM, Rutledge RB. Smartphones and the neuroscience of mental health. Annu Rev Neurosci (in press).
115. Vivian-Griffiths T, Baker E, Schmidt KM et al. Predictive modeling of schizophrenia from genomic data: comparison of polygenic risk score with kernel support vector machines approach. Am J Med Genet B Neuropsychiatr Genet 2019;180:80-5.
116. Kourou K, Exarchos TP, Exarchos KP et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015;13:8-17.
117. Chabon JJ, Hamilton EG, Kurtz DM et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 2020;580:245-51.
118. Iniesta R, Campbell D, Venturini C et al. Gene variants at loci related to blood pressure account for variation in response to antihypertensive drugs between black and white individuals: genomic precision medicine may dispense with ethnicity. Hypertension 2019;74:614-22.
119. Tansey KE, Guipponi M, Hu X et al. Contribution of common genetic variants to antidepressant response. Biol Psychiatry 2013;73:679-82.
120. Lawrie SM. Clinical risk prediction in schizophrenia. Lancet Psychiatry 2014;1:406-8.
121. Zheutlin AB, Ross DA. Polygenic risk scores: what are they good for? Biol Psychiatry 2018;83:e51-3.
122. Amare AT, Schubert KO, Tekola-Ayele F et al. Association of the polygenic scores for personality traits and response to selective serotonin reuptake inhibitors in patients with major depressive disorder. Front Psychiatry 2018;9:65.
123. García-González J, Tansey KE, Hauser J et al. Pharmacogenetics of antidepressant response: a polygenic approach. Prog Neuropsychopharmacol Biol Psychiatry 2017;75:128-34.
124. Ward J, Graham N, Strawbridge R et al. Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: meta-analysis of three treatment cohorts. bioRxiv 2018:295717.
125. Andersson E, Crowley JJ, Lindefors N et al. Genetics of response to cognitive behavior therapy in adults with major depression: a preliminary report. Mol Psychiatry 2019;24:484-90.
126. Li QS, Tian C, Seabrook GR et al. Analysis of 23andMe antidepressant efficacy survey data: implication of circadian rhythm and neuroplasticity in bupropion response. Transl Psychiatry 2016;6:e889.
127. Wigmore EM, Hafferty JD, Hall LS et al. Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP. Pharmacogenomics J 2020;20:329-41.
128. Baune BT, Soda T, Sullivan PF et al. The Genomics of Electroconvulsive Therapy International Consortium (GenECT-ic). Lancet Psychiatry 2019;6:e23.
129. Lin E, Lin C-H, Lane H-Y. Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int J Mol Sci 2020;21:969.
130. Kautzky A, Baldinger P, Souery D et al. The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression. Eur Neuropsychopharmacol 2015;25:441-53.
131. Maciukiewicz M, Marshe VS, Hauschild A-C et al. GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. J Psychiatr Res 2018;99:62-8.
132. Athreya AP, Neavin D, Carrillo-Roa T et al. Pharmacogenomics-driven prediction of antidepressant treatment outcomes: a machine-learning approach with multi-trial replication. Clin Pharmacol Ther 2019;106:855-65.
133. Lin E, Kuo P-H, Liu Y-L et al. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front Psychiatry 2018;9:290.
134. Lee Y, Ragguett RM, Mansur RB et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J Affect Disord 2018;241:519-32.
135. Pisanu C, Squassina A. Treatment-resistant schizophrenia: insights from genetic studies and machine learning approaches. Front Pharmacol 2019;10:1-7.
136. Zubenko GS, Sommer BR, Cohen BM. On the marketing and use of pharmacogenetic tests for psychiatric treatment. JAMA Psychiatry 2018;75:769.
137. Zeier Z, Carpenter LL, Kalin NH et al. Clinical implementation of pharmacogenetic decision support tools for antidepressant drug prescribing. Am J Psychiatry 2018;175:873-86.
138. Enneking V, Leehr EJ, Dannlowski U et al. Brain structural effects of treatments for depression and biomarkers of response: a systematic review of neuroimaging studies. Psychol Med 2020;50:187-209.
139. Colvonen PJ, Glassman LH, Crocker LD et al. Pretreatment biomarkers predicting PTSD psychotherapy outcomes: a systematic review. Neurosci Biobehav Rev 2017;75:140-56.
140. Fonseka TM, MacQueen GM, Kennedy SH. Neuroimaging biomarkers as predictors of treatment outcome in major depressive disorder. J Affect Disord 2018;233:21-35.
141. Fu CH, Steiner H, Costafreda SG. Predictive neural biomarkers of clinical response in depression: a meta-analysis of functional and structural neuroimaging studies of pharmacological and psychological therapies. Neurobiol Dis 2013;52:75-83.
142. Lueken U, Zierhut KC, Hahn T et al. Neurobiological markers predicting treatment response in anxiety disorders: a systematic review and implications for clinical application. Neurosci Biobehav Rev 2016;66:143-62.
143. Molent C, Olivo D, Wolf RC et al. Functional neuroimaging in treatment resistant schizophrenia: a systematic review. Neurosci Biobehav Rev 2019;104:178-90.
144. Tarcijonas G, Sarpal DK. Neuroimaging markers of antipsychotic treatment response in schizophrenia: an overview of magnetic resonance imaging studies. Neurobiol Dis 2019;131:104209.
145. Phillips ML, Chase HW, Sheline YI et al. Identifying predictors, moderators, and mediators of antidepressant response in major depressive disorder: neuroimaging approaches. Am J Psychiatry 2015;172:124-38.
146. Lueken U, Hahn T. Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions. Curr Opin Psychiatry 2016;29:25-31.
147. Khodayari-Rostamabad A, Hasey GM, Maccrimmon DJ et al. A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy. Clin Neurophysiol 2010;121:1998-2006.
148. Erguzel TT, Ozekes S, Gultekin S et al. Neural network based response prediction of rTMS in major depressive disorder using QEEG cordance. Psychiatry Investig 2015;12:61-5.
149. Mumtaz W, Xia L, Mohd Yasin MA et al. A wavelet-based technique to predict treatment outcome for major depressive disorder. PLoS One 2017;12:e0171409.
150. Jaworska N, de la Salle S, Ibrahim MH et al. Leveraging machine learning approaches for predicting antidepressant treatment response using electroencephalography (EEG) and clinical data. Front Psychiatry 2018;9:768.
151. Khodayari-Rostamabad A, Reilly JP, Hasey GM et al. Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression. Annu Int Conf IEEE Eng Med Biol Soc 2011;2011:6418-21.
152. Bailey NW, Hoy KE, Rogasch NC et al. Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures. J Affect Disord 2019;242:68-79.
153. Hasanzadeh F, Mohebbi M, Rostami R. Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. J Affect Disord 2019;256:132-42.
154. Zandvakili A, Philip NS, Jones SR et al. Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: a resting state electroencephalography study. J Affect Disord 2019;252:47-54.
155. Al-Kaysi AM, Al-Ani A, Loo CK et al. Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification. J Affect Disord 2017;208:597-603.
156. Corlier J, Carpenter LL, Wilson AC et al. The relationship between individual alpha peak frequency and clinical outcome with repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD). Brain Stimul 2019;12:1572-8.
157. Woo CW, Chang LJ, Lindquist MA et al. Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 2017;20:365-77.
158. Costafreda SG, Khanna A, Mourao-Miranda J et al. Neural correlates of sad faces predict clinical remission to cognitive behavioural therapy in depression. Neuroreport 2009;20:637-41.
159. Goldstein-Piekarski AN, Korgaonkar MS, Green E et al. Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants. Proc Natl Acad Sci USA 2016;113:11955-60.
160. Hahn T, Kircher T, Straube B et al. Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry 2015;72:68-74.
161. Ball TM, Stein MB, Ramsawh HJ et al. Single-subject anxiety treatment outcome prediction using functional neuroimaging. Neuropsychopharmacology 2014;39:1254-61.
162. Tolmeijer E, Kumari V, Peters E et al. Using fMRI and machine learning to predict symptom improvement following cognitive behavioural therapy for psychosis. Neuroimage Clin 2018;20:1053-61.
163. Crane NA, Jenkins LM, Bhaumik R et al. Multidimensional prediction of treatment response to antidepressants with cognitive control and functional MRI. Brain 2017;140:472-86.
164. Nguyen KP, Fatt CC, Treacher A et al. Predicting response to the antidepressant bupropion using pretreatment fMRI. Predict Intell Med 2019;11843:53-62.
165. van Waarde JA, Scholte HS, van Oudheusden LJ et al. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 2015;20:609-14.
166. Whitfield-Gabrieli S, Ghosh SS, Nieto-Castanon A et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry 2016;21:680-5.
167. Zhutovsky P, Thomas RM, Olff M et al. Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data. Transl Psychiatry 2019;9:326.
168. Yuan M, Qiu C, Meng Y et al. Pre-treatment resting-state functional MR imaging predicts the long-term clinical outcome after short-term paroxetine treatment in post-traumatic stress disorder. Front Psychiatry 2018;9:532.
169. Cao B, Cho RY, Chen D et al. Treatment response prediction and individualized identification of first-episode drug-naïve schizophrenia using brain functional connectivity. Mol Psychiatry 2020;25:906-13.
170. Leaver AM, Wade B, Vasavada M et al. Fronto-temporal connectivity predicts ECT outcome in major depression. Front Psychiatry 2018;9:92.
171 . Redlich R, Opel N, Grotegerd D et al. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry 2016;73:557-64.
172. Cao B, Luo Q, Fu Y et al. Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder. Sci Rep 2018;8:5434-4.
173. Gong J, Cui LB, Xi YB et al. Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020;216:262-71.
174. Germine L, Strong RW, Singh S et al. Toward dynamic phenotypes and the scalable measurement of human behavior. Neuropsychopharmacology 2021;46:209-16.
175. Etkin A, Patenaude B, Song YJ et al. A cognitive-emotional biomarker for predicting remission with antidepressant medications: a report from the iSPOT-D trial. Neuropsychopharmacology 2015;40:1332-42.
176. Rennie JP, Zhang M, Hawkins E et al. Mapping differential responses to cognitive training using machine learning. Dev Sci 2020;23:e12868.
177. Delgadillo J, Gonzalez Salas Duhne P. Targeted prescription of cognitive–behavioral therapy versus person-centered counseling for depression using a machine learning approach. J Consult Clin Psychol 2020;88:14-24.
178. Schwartz B, Cohen ZD, Rubel JA et al. Personalized treatment selection in routine care: integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy. Psychother Res 2021;31:33-51.
179. Delgadillo J, Ali S, Fleck K et al. StratCare: a pragmatic, multi-site, single-blind, cluster randomised controlled trial of stratified care for depression. Unpublished manuscript.
180. Lutz W, Deisenhofer AK, Rubel J et al. Prospective evaluation of a clinical decision support system in psychological therapy. Unpublished manuscript.
181. Browning M, Bilderbeck AC, Dias R et al. The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label , randomised controlled trial. Neuropsychopharmacology (in press).
182. Jussim L, Harber KD. Teacher expectations and self-fulfilling prophecies: knowns and unknowns, resolved and unresolved controversies. Personal Soc Psychol Rev 2005;9:131-55.
183. Patel SR, Bakken S, Ruland C. Recent advances in shared decision making for mental health. Curr Opin Psychiatry 2008;21:606-12.
184. Lundberg SM, Lee S-I. A Unified approach to interpreting model predictions. arXiv 2017;1705.07874v2.
185. Chekroud AM. Pragmatic, scalable, computational solutions to reduce the burden of major depression. ProQuest; 2018;10907745.
186. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019;1:206-15.
187. Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J et al. Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice. Schizophr Bull (in press).
188. Jacobs M, Pradier MF, McCoy TH Jr et al. How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection. Transl Psychiatry 2021;11:108.
189. Delgadillo J, Appleby S, Booth S et al. The Leeds Risk Index: field-test of a stratified psychological treatment selection algorithm. Psychother Psychosom 2020;89:189-90.
190. Müller VC. Ethics of artificial intelligence and robotics. https://plato.stanford.edu/entries/ethics-ai/.
191. Lo Piano S. Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanit Soc Sci Commun 2020;7:9.
192. Mittelstadt BD, Floridi L. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics 2016;22:303-41.
193. Starke G, De Clercq E, Borgwardt S et al. Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychol Med (in press).
194. Trujillo AC, Gregory IM, Ackerman KA. Evolving relationship between humans and machines. IFAC-PapersOnLine 2019;51:366-71.
195. Endsley MR. Toward a theory of situation awareness in dynamic systems. Hum Factors 1995;37:32-64.
196. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56.
197. Haque OS, Waytz A. Dehumanization in medicine: causes, solutions, and functions. Perspect Psychol Sci 2012;7:176-86.
198. Chen N-C, Drouhard M, Kocielnik R et al. Using machine learning to support qualitative coding in social science: shifting the focus to ambiguity. ACM Trans Interact Intell Syst 2018;8:1-20.
199. Stewart M. Towards a global definition of patient centred care: the patient should be the judge of patient centred care. BMJ 2001;322:444-5.
200. Robin R, Polanyi M. Personal knowledge. towards a post-critical philosophy. Philos Phenomenol Res 1960;20:429.
201. Bennett NL. Donald A. Schön, educating the reflective practitioner. San Francisco: Jossey-Bass, 1987.
202. Borrell-Carrió F, Suchman A, Epstein RM. The biopsychosocial model 25 years later: principles, practice, and scientific inquiry. Ann Fam Med 2004;2:576-82.
203. Faden RR, Kass NE, Goodman SN et al. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. Hastings Cent Rep 2013;43:S16-27.
204. Boff KR. Revolutions and shifting paradigms in human factors & ergonomics. Appl Ergon 2006;37:391-9.
205. Brangier E, Hammes-Adelé S. Beyond the technology acceptance model: elements to validate the human-technology symbiosis model. In: Robertson MM (eds). Ergonomics and health aspects of work with computers. Berlin: Springer, 2011:13-21.
206. Christophe G, Jean-Arthur M-F, Guillaume D. Comment on Starke et al.: ‘Computing schizophrenia: ethical challenges for machine learning in psychiatry’: from machine learning to student learning: pedagogical challenges for psychiatry. Psychol Med (in press).
207. Gerber A, Derckx P, Döppner DA et al. Conceptualization of the human-machine symbiosis – A literature review. Presented at the 53rd Hawaii International Conference on System Sciences, Maui, January 2020.
208. Floridi L, Taddeo M. What is data ethics? Philos Trans R Soc Math Phys Eng Sci 2016;374:2083.
209. Binns R. Fairness in machine learning: lessons from political philosophy. Proceedings of Machine Learning Research 2018;81:149-59.
210. Obermeyer Z, Powers B, Vogeli C et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366:447-53.
211. Yeung K, Lodge M (eds). Algorithmic regulation. Oxford: Oxford University Press, 2019.
212. Brownsword R, Scotford E, Yeung K (eds). The Oxford handbook of law, regulation and technology, Vol. 1. Oxford: Oxford University Press, 2016.
213. Jacucci G, Spagnolli A, Freeman J et al. Symbiotic interaction: a critical definition and comparison to other human-computer paradigms. Presented at the International Workshop on Symbiotic Interaction, Helsinki, October 2014.
214. Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med 2010;51:214-21.
215. Lustria MLA, Noar SM, Cortese J et al. A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun 2013;18:1039-69.
216. Bird S, Kenthapadi K, Kiciman E et al. Fairness-aware machine learning: practical challenges and lessons learned. Presented at the 12th ACM International Conference on Web Search and Data Mining, Melbourne, February 2019.
217. Koutsouleris N, Dwyer DB, Degenhardt F et al. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry 2021;78:195-209.

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