Binge Eating, Purging, and Restriction Symptoms: Increasing Accuracy of Prediction Using Machine Learning

Behavior Therapy - Tập 54 - Trang 247-259 - 2023
Cheri A. Levinson1, Christopher M. Trombley1, Leigh C. Brosof1, Brenna M. Williams1, Rowan A. Hunt1
1University of Louisville

Tài liệu tham khảo

American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596. Askland, 2015, Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy, International Journal of Methods in Psychiatric Research, 24, 156, 10.1002/mpr.1463 Barenholtz, 2020, Machine-learning approaches to substance-abuse research: Emerging trends and their implications, Current Opinion in Psychiatry, 33, 334, 10.1097/YCO.0000000000000611 Berkman, 2007, Outcomes of eating disorders: A systematic review of the literature, International Journal of Eating Disorders, 40, 293, 10.1002/eat.20369 Burke, 2019, The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review, Journal of Affective Disorders, 245, 869, 10.1016/j.jad.2018.11.073 Chawla, 2002, SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, 16, 321, 10.1613/jair.953 Chekroud, 2016, Cross-trial prediction of treatment outcome in depression: A machine learning approach, The Lancet Psychiatry, 3, 243, 10.1016/S2215-0366(15)00471-X Espel-Huynh, 2021, Prediction of eating disorder treatment response trajectories via machine learning does not improve performance versus a simpler regression approach, International Journal of Eating Disorders, 54, 1250, 10.1002/eat.23510 Fairburn, 2003, Cognitive behaviour therapy for eating disorders: A “transdiagnostic” theory and treatment, Behaviour Research and Therapy, 41, 509, 10.1016/S0005-7967(02)00088-8 First, 2015 Fox, 2019, Model complexity improves the prediction of nonsuicidal self-injury, Journal of Consulting and Clinical Psychology, 87, 684, 10.1037/ccp0000421 Forney, 2016, The medical complications associated with purging, International Journal of Eating Disorders, 49, 249, 10.1002/eat.22504 Forrest, 2021, Machine learning v. traditional regression models predicting treatment outcomes for binge-eating disorder from a randomized controlled trial, Psychological Medicine, 1, 10.1017/S0033291721004748 Galatzer-Levy, 2017, Utilization of machine learning for prediction of post-traumatic stress: A re-examination of cortisol in the prediction and pathways to non-remitting PTSD, Translational Psychiatry, 7, e1070, 10.1038/tp.2017.38 Gao, 2018, Machine learning in major depression: From classification to treatment outcome prediction, CNS Neuroscience & Therapeutics, 24, 1037, 10.1111/cns.13048 Garfinkel, 1980, The heterogeneity of anorexia nervosa: Bulimia as a distinct subgroup, Archives of General Psychiatry, 37, 1036, 10.1001/archpsyc.1980.01780220074008 Guarda, 2008, Treatment of anorexia nervosa: Insights and obstacles, Physiology & Behavior, 94, 113, 10.1016/j.physbeh.2007.11.020 Guo, 2016, Machine learning derived risk prediction of anorexia nervosa, BMC Medical Genomics, 9, 4, 10.1186/s12920-016-0165-x Haynos, 2021, Machine learning enhances prediction of illness course: A longitudinal study in eating disorders, Psychological Medicine, 51, 1392, 10.1017/S0033291720000227 Ioannidis, 2020, Early warning systems in inpatient anorexia nervosa: A validation of the MARSIPAN-based modified early warning system, European Eating Disorder Review, 28, 551, 10.1002/erv.2753 Keel, 2006, Point prevalence of bulimia nervosa in 1982, 1992, and 2002, Psychological Medicine, 36, 119, 10.1017/S0033291705006148 Kessler, 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, 10.1002/wps.20150 Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. In I. Maglogiannis (Ed.), Emerging artificial intelligence applications in computer engineering (pp. 3–24). IOS Press. Larochelle, 2018, Medication for opioid use disorder after nonfatal opioid overdose and association with mortality: A cohort study, Annals of Internal Medicine, 169, 137, 10.7326/M17-3107 Lecrubier, 1997, Mini International Neuropsychiatric Interview (MINI) [Database record], APA PsycTests Lenhard, 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, e1576, 10.1002/mpr.1576 Levinson, 2018, Meal and snack-time eating disorder cognitions predict eating disorder behaviors and vice versa in a treatment seeking sample: A mobile technology based ecological momentary assessment study, Behaviour Research and Therapy, 105, 36, 10.1016/j.brat.2018.03.008 Linardon, 2017, Predictors, moderators, and mediators of treatment outcome following manualised cognitive-behavioural therapy for eating disorders: A systematic review, European Eating Disorder Review, 25, 3, 10.1002/erv.2492 Linardon, 2020, Interactions between different eating patterns on recurrent binge-eating behavior: A machine learning approach, International Journal of Eating Disorders, 53, 533, 10.1002/eat.23232 Linthicum, 2019, Machine learning in suicide science: Applications and ethics, Behavioral Sciences & the Law, 37, 214, 10.1002/bsl.2392 Lundberg, 2017, A unified approach to interpreting model predictions, 4765 Månsson, 2015, Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning, Translational Psychiatry, 5, e530, 10.1038/tp.2015.22 Mason, 2021, Examination of momentary maintenance factors and eating disorder behaviors and cognitions using ecological momentary assessment, Eating Disorders: The Journal of Treatment & Prevention, 29, 42, 10.1080/10640266.2019.1613847 Pedregosa, 2011, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, 12, 2825 Pérez, 2007, IPython: A system for interactive scientific computing, Computing in Science & Engineering, 9, 21, 10.1109/MCSE.2007.53 Sasikala, 2016, Detection and prediction of seizures using a wrist-based wearable platform, Journal of Chemical and Pharmaceutical Sciences, 9, 3208 Sadeh-Sharvit, 2020, Predicting eating disorders from Internet activity, International Journal of Eating Disorders, 53, 1526, 10.1002/eat.23338 Schaefer, 2020, Ecological momentary assessment in eating disorders research: Recent findings and promising new directions, Current Opinion in Psychiatry, 33, 528, 10.1097/YCO.0000000000000639 Shalev-Shwartz, 2014 Smink, 2012, Epidemiology of eating disorders: Incidence, prevalence and mortality rates, Current Psychiatry Reports, 14, 406, 10.1007/s11920-012-0282-y Stice, 1996, Test of the dual pathway model of bulimia nervosa: Evidence for dietary restraint and affect regulation mechanisms, Journal of Social and Clinical Psychology, 15, 340, 10.1521/jscp.1996.15.3.340 Sysko, 2010, Heterogeneity moderates treatment response among patients with binge eating disorder, Journal of Consulting and Clinical Psychology, 78, 681, 10.1037/a0019735 Van Buuren, 2011, mice: Multivariate imputation by chained equations in R, Journal of Statistical Software, 45, 1 Van Der Walt, 2011, The NumPy array: A structure for efficient numerical computation, Computing in Science & Engineering, 13, 22, 10.1109/MCSE.2011.37 Walsh, 2017, Predicting risk of suicide attempts over time through machine learning, Clinical Psychological Science, 5, 457, 10.1177/2167702617691560 Wang, 2021, Machine learning to advance the prediction, prevention and treatment of eating disorders, European Eating Disorders Review, 29, 683, 10.1002/erv.2850 Westmoreland, 2016, Medical complications of anorexia nervosa and bulimia, The American Journal of Medicine, 129, 30, 10.1016/j.amjmed.2015.06.031 Yarkoni, 2017, Choosing prediction over explanation in psychology: Lessons from machine learning, Perspectives on Psychological Science, 12, 1100, 10.1177/1745691617693393