Published on in Vol 8, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24522, first published .
Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study

Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study

Using Machine Learning Imputed Outcomes to Assess Drug-Dependent Risk of Self-Harm in Patients with Bipolar Disorder: A Comparative Effectiveness Study

Journals

  1. Zhao Y, Yu Y, Wang H, Li Y, Deng Y, Jiang G, Luo Y. Machine Learning in Causal Inference: Application in Pharmacovigilance. Drug Safety 2022;45(5):459 View
  2. Pisanu C, Vitali E, Meloni A, Congiu D, Severino G, Ardau R, Chillotti C, Trabucchi L, Bortolomasi M, Gennarelli M, Minelli A, Squassina A. Investigating the Role of Leukocyte Telomere Length in Treatment-Resistant Depression and in Response to Electroconvulsive Therapy. Journal of Personalized Medicine 2021;11(11):1100 View
  3. Pigoni A, Delvecchio G, Turtulici N, Madonna D, Pietrini P, Cecchetti L, Brambilla P. Machine learning and the prediction of suicide in psychiatric populations: a systematic review. Translational Psychiatry 2024;14(1) View
  4. Atmakuru A, Shahini A, Chakraborty S, Seoni S, Salvi M, Hafeez-Baig A, Rashid S, Tan R, Barua P, Molinari F, Acharya U. Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion 2025;114:102673 View