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

New Methods (909)

Published on in Vol 20, No 8 (2018): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10275, first published .
Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Authors of this article:

Vincent Bremer1 Author Orcid Image ;   Dennis Becker1 Author Orcid Image ;   Spyros Kolovos2, 3 Author Orcid Image ;   Burkhardt Funk1 Author Orcid Image ;   Ward van Breda4 Author Orcid Image ;   Mark Hoogendoorn4 Author Orcid Image ;   Heleen Riper2 Author Orcid Image
Article Authors Cited by (25) Tweetations (7) Metrics

Journals

  1. Naghibi S, Ghassemi F, Maleki A, Fallah A. The Effects of Upper Limb Motor Recovery on Submovement Characteristics among the Patients with Stroke: A Meta‐Analysis. PM&R 2020;12(6):589 View
  2. Friedl N, Krieger T, Chevreul K, Hazo J, Holtzmann J, Hoogendoorn M, Kleiboer A, Mathiasen K, Urech A, Riper H, Berger T. Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care. Journal of Clinical Medicine 2020;9(2):490 View
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  4. Wolff J, Pauling J, Keck A, Baumbach J. Systematic Review of Economic Impact Studies of Artificial Intelligence in Health Care. Journal of Medical Internet Research 2020;22(2):e16866 View
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  8. Kuo C, Chiu H. Application of artificial intelligence in gastroenterology: Potential role in clinical practice. Journal of Gastroenterology and Hepatology 2021;36(2):267 View
  9. Guo H, Li J, Liu H, He J. Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study. BMC Medical Informatics and Decision Making 2022;22(1) View
  10. Zhou Y, Chen X, Liu D, Pan Y, Hou Y, Gao T, Peng F, Wang X, Zhang X. Predicting first session working alliances using deep learning algorithms: A proof-of-concept study for personalized psychotherapy. Psychotherapy Research 2022;32(8):1100 View
  11. Gomez Rossi J, Feldberg B, Krois J, Schwendicke F. Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis. JMIR Medical Informatics 2022;10(8):e33703 View
  12. 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
  13. Voets M, Veltman J, Slump C, Siesling S, Koffijberg H. Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah. Value in Health 2022;25(3):340 View
  14. White K, Belachew B. Role of Psychologists in Pediatric Subspecialties. Pediatric Clinics of North America 2022;69(5):825 View
  15. Hornstein S, Forman-Hoffman V, Nazander A, Ranta K, Hilbert K. Predicting therapy outcome in a digital mental health intervention for depression and anxiety: A machine learning approach. DIGITAL HEALTH 2021;7:205520762110606 View
  16. Marti-Puig P, Capra C, Vega D, Llunas L, Solé-Casals J. A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults. Sensors 2022;22(13):4790 View
  17. 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
  18. 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
  19. Abuhay T, Robinson S, Mamuye A, Kovalchuk S. Machine learning integrated patient flow simulation: why and how?. Journal of Simulation 2023;17(5):580 View
  20. Huang S, Wahlquist A, Dahne J. Individual Predictors of Response to A Behavioral Activation-Based Digital Smoking Cessation Intervention: A Machine Learning Approach. Substance Use & Misuse 2024:1 View

Books/Policy Documents

  1. Wang Z, Niu Z, Yang L, Cui L. Depressive Disorders: Mechanisms, Measurement and Management. View
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