This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (2109) Depression and Mood Disorders; Suicide Prevention (1029) Anxiety and Stress Disorders (811) Machine Learning (1004) Myocardial Infarction (Heart Attack) (33) Engagement with and Adherence to Digital Health Interventions, Law of Attrition (368)

Published on in Vol 20 , No 10 (2018) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10754, first published .
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

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

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

Authors of this article:

John Wallert 1 Author Orcid Image ;   Emelie Gustafson 1 Author Orcid Image ;   Claes Held 2, 3 Author Orcid Image ;   Guy Madison 4 Author Orcid Image ;   Fredrika Norlund 1 Author Orcid Image ;   Louise von Essen 1 Author Orcid Image ;   Erik Martin Gustaf Olsson 1 Author Orcid Image
Article Authors Cited by (28) Tweetations (6) Metrics

Journals

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  6. Schover L, Strollo S, Stein K, Fallon E, Smith T. Effectiveness trial of an online self-help intervention for sexual problems after cancer. Journal of Sex & Marital Therapy 2020;46(6):576 View
  7. Bischoff T, Hynes K, Tambling R, Kingzette A. Marriage and Family Therapists’ Reporting of Telehealth Use on Practice Websites during COVID-19: A Linguistic Analysis. The American Journal of Family Therapy 2022;50(2):159 View
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  10. Humphries S, Wallert J, Norlund F, Wallin E, Burell G, von Essen L, Held C, Olsson E. Internet-Based Cognitive Behavioral Therapy for Patients Reporting Symptoms of Anxiety and Depression After Myocardial Infarction: U-CARE Heart Randomized Controlled Trial Twelve-Month Follow-up. Journal of Medical Internet Research 2021;23(5):e25465 View
  11. Chekroud A, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021;20(2):154 View
  12. Zhang Q, Hu Q, Li Y, Sun Y, He J, Qiu M, Zhang J, Liang Y, Han Y. Efficacy of CPET Combined with Systematic Education of Cardiac Rehabilitation After PCI: A Real-World Evaluation in ACS Patients. Advances in Therapy 2021;38(9):4836 View
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  15. Pizga A, Karatzanos E, Tsikrika S, Gioni V, Vasileiadis I, Nanas S, Kordoutis P. Psychosocial Interventions to Enhance Treatment Adherence to Lifestyle Changes in Cardiovascular Disease: A Review of the Literature 2011-2021. European Journal of Environment and Public Health 2022;6(1):em0102 View
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  17. Linardon J, Fuller‐Tyszkiewicz M, Shatte A, Greenwood C. An exploratory application of machine learning methods to optimize prediction of responsiveness to digital interventions for eating disorder symptoms. International Journal of Eating Disorders 2022;55(6):845 View
  18. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  19. Linnet J, Hertz S, Jensen E, Runge E, Tarp K, Holmberg T, Mathiasen K, Lichtenstein M. Days between sessions predict attrition in text-based internet intervention of Binge Eating Disorder. Internet Interventions 2023;31:100607 View
  20. Fife D, D’Onofrio J. Common, uncommon, and novel applications of random forest in psychological research. Behavior Research Methods 2022 View
  21. Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Mental Health 2023;10:e42045 View
  22. Wallert J, Boberg J, Kaldo V, Mataix-Cols D, Flygare O, Crowley J, Halvorsen M, Ben Abdesslem F, Boman M, Andersson E, Hentati Isacsson N, Ivanova E, Rück C. Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data. Translational Psychiatry 2022;12(1) View
  23. Linnet J, Jensen E, Runge E, Hansen M, Hertz S, Mathiasen K, Lichtenstein M. Text based internet intervention of Binge Eating Disorder (BED): Words per message is associated with treatment adherence. Internet Interventions 2022;28:100538 View
  24. Torres R, Zurita C, Mellado D, Nicolis O, Saavedra C, Tuesta M, Salinas M, Bertini A, Pedemonte O, Querales M, Salas R. Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning. Diagnostics 2023;13(3):508 View
  25. Dai R, Kannampallil T, Zhang J, Lv N, Ma J, Lu C. Multi-Task Learning for Randomized Controlled Trials. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View

Books/Policy Documents

  1. Safinianaini N, Boström H, Kaldo V. Artificial Intelligence in Medicine. View
  2. Harrer M, Terhorst Y, Baumeister H, Ebert D. Digitale Gesundheitsinterventionen. View