A roadmap for applying machine learning when working with privacy-sensitive data: predicting non-response to treatment for eating disorders

References

• Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuro psycho pharmacol. 2021;46(1):176–190. doi: 10.1038/s41386-020-0767-z
   Google Scholar
• McMahon FJ. Prediction of treatment outcomes in psychiatry—where do we stand? Dialogues Clin Neurosci. 2014;16(4):455. doi: 10.31887/DCNS.2014.16.4/fmcmahon
   PubMed Web of Science ®Google Scholar
• Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14(1):91–118. doi: 10.1146/annurev-clinpsy-032816-045037
   PubMedGoogle Scholar
• Cearns M, Hahn T, Baune BT. Recommendations and future directions for supervised machine learning in psychiatry. Transl Psychiatry. 2019;9(1):1–12. doi: 10.1038/s41398-019-0607-2
   PubMedGoogle Scholar
• Kuhn M, Johnson K. Introduction. applied predictive modelling. (NY): Springer New York; 2013. p. 1–16. doi: 10.1007/978-1-4614-6849-3_1
   Google Scholar
• Poldrack RA, Huckins G, Varoquaux G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry. 2020 May 1;77(5):534–540. doi: 10.1001/jamapsychiatry.2019.3671
   PubMed Web of Science ®Google Scholar
• Salazar de Pablo G, Studerus E, Vaquerizo-Serrano J, et al. Implementing precision psychiatry: a systematic review of individualized prediction models for clinical practice. Schizophrenia Bulletin. 2021 Mar 16;47(2):284–297. doi: 10.1093/schbul/sbaa120
   PubMed Web of Science ®Google Scholar
• Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349(6245):255–260. doi: 10.1126/science.aaa8415
   PubMed Web of Science ®Google Scholar
• Rutledge RB, Chekroud AM, Huys QJ. Machine learning and big data in psychiatry: toward clinical applications. Curr Opin Neurobiol. 2019;55:152–159. doi: 10.1016/j.conb.2019.02.006
   PubMed Web of Science ®Google Scholar
• Iniesta R, Stahl D, McGuffin P. Machine learning, statistical learning and the future of biological research in psychiatry. Psychol Med change. 2016;46(12):2455–2465. doi: 10.1017/S0033291716001367
   PubMed Web of Science ®Google Scholar
• Chekroud AM, Zotti RJ, Shehzad Z, et al. Cross-trial prediction of treatment outcome in depression: a machine learning approach. Lancet Psychiatry. 2016;3(3):243–250. doi: 10.1016/S2215-0366(15)00471-X
   PubMed Web of Science ®Google Scholar
• Wiens J, Shenoy ES. Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin Infect Dis. 2018 Jan 6;66(1):149–153. doi: 10.1093/cid/cix731
   PubMed Web of Science ®Google Scholar
• Andersen AEDiagnosis and treatment of the eating disorders spectrum in primary care medicine. In: Mehler PS, and Anderson AE, editor. Eating disorders: a guide to medical care and complications 3 ed. Baltimore, Maryland, USA: Johns Hopkins University Press; 2017, p. 1–55.
   Google Scholar
• Klump KL, Bulik CM, Kaye WH, et al. Academy for eating disorders position paper: eating disorders are serious mental illnesses. Int J Eat Disord. 2009;42(2):97–103. doi: 10.1002/eat.20589
   PubMed Web of Science ®Google Scholar
• Halmi KA. Perplexities of treatment resistance in eating disorders. BMC Psychiatry. 2013;13(1):1–6. doi: 10.1186/1471-244X-13-292
   PubMedGoogle Scholar
• Fassino S, Abbate-Daga G. Resistance to treatment in eating disorders: a critical challenge. BMC Psychiatry. 2013 Nov 7;13(1):282. doi: 10.1186/1471-244X-13-282
   PubMedGoogle Scholar
• Halmi KA, Agras WS, Crow S, et al. Predictors of treatment acceptance and completion in anorexia nervosa: implications for future study designs. Arch Gen Psychiatry. 2005 Jul;62(7):776–781.
   PubMed Web of Science ®Google Scholar
• Linardon J, Wade TD. How many individuals achieve symptom abstinence following psychological treatments for bulimia nervosa? A meta-analytic review. Int J Eat Disord. 2018 Apr;51(4):287–294. doi: 10.1002/eat.22838
   PubMed Web of Science ®Google Scholar
• Le Grange D, Accurso EC, Lock J, et al. Early weight gain predicts outcome in two treatments for adolescent anorexia nervosa. Int J Eat Disord. 2014 Mar;47(2):124–129.
   PubMed Web of Science ®Google Scholar
• Linardon J, de la Piedad Garcia X, Brennan L. Predictors, moderators, and mediators of treatment outcome following manualised cognitive-behavioural therapy for eating disorders: a systematic review. Eur Eat Disorders Rev. 2017 Jan;25(1):3–12. doi: 10.1002/erv.2492
   PubMed Web of Science ®Google Scholar
• Wang SB. Machine learning to advance the prediction, prevention and treatment of eating disorders. Euro Eating Disorders Rev. 2021 Sep;29(5):683–691. doi: 10.1002/erv.2850
   PubMed Web of Science ®Google Scholar
• Haynos AF, Wang SB, Lipson S, et al.Machine learning enhances prediction of illness course: a longitudinal study in eating disorders. Psychol Med. 2021;58(8):1392–1402. doi:10.1017/S0033291720000227
   Google Scholar
• Espel-Huynh H, Zhang F, Thomas JG, et al. Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach. Int J Eat Disord. 2021 Jul;54(7):1250–1259.
   PubMed Web of Science ®Google Scholar
• Balthazar P, Harri P, Prater A, et al. Protecting your patients’ interests in the era of big data, artificial intelligence, and predictive analytics. J Am Coll Radiol. 2018;15(3):580–586. doi: 10.1016/j.jacr.2017.11.035
   PubMed Web of Science ®Google Scholar
• Quintana DS. A synthetic dataset primer for the biobehavioural sciences to promote reproducibility and hypothesis generation. Elife. 2020;9:e53275.
   PubMed Web of Science ®Google Scholar
• Nowok B, Raab GM, Dibben C. Synthpop: bespoke creation of synthetic data in R. J Stat Soft. 2016;74(11):1–26. doi: 10.18637/jss.v074.i11
   Web of Science ®Google Scholar
• Emam KE, Mosquera L, Zheng C. Optimizing the synthesis of clinical trial data using sequential trees. J Am Med Inform Assoc. 2021 Jan 15;28(1):3–13. doi: 10.1093/jamia/ocaa249
   PubMed Web of Science ®Google Scholar
• El Emam K, Mosquera L, Bass J. Evaluating identity disclosure risk in fully synthetic health data: model development and validation. J Med Internet Res. 2020;22(11):e23139. doi: 10.2196/23139
   PubMed Web of Science ®Google Scholar
• Kuhn M. A Short Introduction to the caret Package. R Found Stat Comput. 2013. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=768f3fead8440788ce69dedf67220de93ab2bacd
   Google Scholar
• James G, Witten D, Hastie T, et al. An introduction to statistical learning: with applications in R. (NY): Springer New York; 2013. doi: 10.1007/978-1-4614-7138-7
   Google Scholar
• Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B (Methodological). 1996;58(1):267–288. doi: 10.1111/j.2517-6161.1996.tb02080.x
   Google Scholar
• Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• De Vos JA, Netten C, Noordenbos G. Recovered eating disorder therapists using their experiential knowledge in therapy: a qualitative examination of the therapists’ and the patients’ view. Eat Disord. 2016;24(3):207–223. doi: 10.1080/10640266.2015.1090869
   PubMed Web of Science ®Google Scholar
• De Vos JA, Radstaak M, Bohlmeijer ET, et al. Having an eating disorder and still being able to flourish? Examination of pathological symptoms and well-being as two continua of mental health in a clinical sample. Front Psychol. 2018;9:2145. doi:10.3389/fpsyg.2018.02145
   PubMed Web of Science ®Google Scholar
• de VosJA, Radstaak M, Bohlmeijer ET, et al. The psychometric network structure of mental health in eating disorder patients. Eur Eat Disorders Rev. 2021;29(4):559–574. doi: 10.1002/erv.2832
   PubMed Web of Science ®Google Scholar
• Fairburn CG, Beglin SJ. Assessment of eating disorders: interview or self‐report questionnaire? Int J Eat Disord. 1994;16(4):363–370.
   PubMed Web of Science ®Google Scholar
• Lamers SM, Glas CA, Westerhof GJ, et al. Longitudinal evaluation of the mental health continuum-short form (MHC-SF). Eur J Psychol Assess. 2012;28(4):290–296. doi: 10.1027/1015-5759/a000109
   Web of Science ®Google Scholar
• Lambert MJ, Burlingame GM, Umphress V, et al. The reliability and validity of the outcome questionnaire. Clin Psychol Psychother. 1996;3(4):249–258. doi: 10.1002/(SICI)1099-0879(199612)3:4<249:AID-CPP106>3.0.CO;2-S
   Web of Science ®Google Scholar
• Vander Zee KI, Sanderman R, Heyink JW, et al. Psychometric qualities of the RAND 36-Item Health Survey 1.0: a multidimensional measure of general health status. Int J Behav Med. 1996;3(2):104–122. doi: 10.1207/s15327558ijbm0302_2
   PubMed Web of Science ®Google Scholar
• Van Dierendonck D. The construct validity of Ryff’s scales of psychological well-being and its extension with spiritual well-being. Pers Individ Dif. 2004;36(3):629–643. doi: 10.1016/S0191-8869(03)00122-3
   Web of Science ®Google Scholar
• Gilbert P, Clarke M, Hempel S, et al. Criticizing and reassuring oneself: an exploration of forms, styles and reasons in female students. Br J Clin Psychol. 2004;43(1):31–50. doi: 10.1348/014466504772812959
   PubMed Web of Science ®Google Scholar
• Rosenberg M. Rosenberg self-esteem scale (RSE). Acceptance and commitment therapy Measures package. Measures Package. 1965;61(52):18.
   Google Scholar
• Krueger RF, Derringer J, Markon KE, et al. Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychol Med. 2012;42(9):1879. doi: 10.1017/S0033291711002674
   PubMed Web of Science ®Google Scholar
• de JongM, Spinhoven P, Korrelboom K, et al. Effectiveness of enhanced cognitive behavior therapy for eating disorders: a randomized controlled trial. Int J Eat Disord. 2020 May;53(5):447–457.
   PubMed Web of Science ®Google Scholar
• Serafini G, Parisi VM, Aguglia A, et al. A specific inflammatory profile underlying suicide risk? Systematic review of the main literature findings. Int J Environ Res Public Health. 2020 Apr 1;17(7):2393. doi: 10.3390/ijerph17072393
   PubMed Web of Science ®Google Scholar
• Prosperi M, Guo Y, Sperrin M, et al. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Mach Intell. 2020;2(7):369–375. doi: 10.1038/s42256-020-0197-y
   Google Scholar
• Chen JH, Asch SM. Machine learning and prediction in medicine—beyond the peak of inflated expectations. N Engl J Med. 2017;376(26):2507. doi: 10.1056/NEJMp1702071
   PubMed Web of Science ®Google Scholar
• Tomba E, Tecuta L, Schumann R, et al. Does psychological well-being change following treatment? An exploratory study on outpatients with eating disorders. Compr Psychiatry. 2017;74:61–69. doi: 10.1016/j.comppsych.2017.01.001
   PubMed Web of Science ®Google Scholar
• Bardone-Cone AM, Hunt RA, Watson HJ. An overview of conceptualizations of eating disorder recovery, recent findings, and future directions. Curr Psychiatry Rep. 2018 8;20(9):79. doi: 10.1007/s11920-018-0932-9
   PubMed Web of Science ®Google Scholar
• Vall E, Wade TD. Predictors of treatment outcome in individuals with eating disorders: a systematic review and meta‐analysis. Int J Eat Disord. 2015;48(7):946–971. doi: 10.1002/eat.22411
   PubMed Web of Science ®Google Scholar
• Kuhn M, Johnson K. Remedies for severe class imbalance. Applied predictive modeling. (NY): Springer New York; 2013. p. 419–443. doi: 10.1007/978-1-4614-6849-3_16
   Google Scholar