Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

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

(2019) Chul-Hyun Cho et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Using Digital Phenotyping to Accurately Detect Depression Severity

(2019) Nicholas C. Jacobson et al.   JOURNAL OF NERVOUS AND MENTAL DISEASE

Real-time Mobile Monitoring of the Dynamic Associations Among Motor Activity, Energy, Mood, and Sleep in Adults With Bipolar Disorder

(2018) Kathleen Ries Merikangas et al.   JAMA Psychiatry

Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study

(2016) Joost Asselbergs et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

(2015) Sohrab Saeb et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features

(2011) Ozgur Demir-Kavuk et al.   BMC BIOINFORMATICS