Accessibility settings

Published on in Vol 21, No 10 (2019): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/15966, first published .
JMIR Publications logo: Advancing Digital Health & Open Science

Addendum to the Acknowledgements: Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Addendum to the Acknowledgements: Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study

Journals

  1. Jeong I, Kong S, Kim Y, Kim Y, Kim B, Ahn S, Kim J, Lee H. Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data. Journal of Medical Systems 2025;49(1) View
  2. Halabi R, Alda M, Tolend M, O’Donovan C, Hintze A, Mulsant B, Ortiz A. High accuracy of passive monitoring for detecting hypomanic symptoms in bipolar disorder: an explainable machine learning approach (Preprint). JMIR mHealth and uHealth 2026 View

Conference Proceedings

  1. Gao J, Yan R, Doryab A. 2023 International Conference on Machine Learning and Applications (ICMLA). Modeling Regularity and Predictability in Human Behavior from Multidimensional Sensing Signals and Personal Characteristics View
  2. Agnihotri N, Prasad S. 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART). Predicting the Symptoms of Bipolar Disorder in Patients using Machine Learning View