Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study-Reference-Cited by-同舟云学术

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

Published:2019-04-17 Issue:4 Volume:21 Page:e11029
ISSN:1438-8871
Container-title:Journal of Medical Internet Research
language:en
Short-container-title:J Med Internet Res

Author:

Cho Chul-Hyun^ORCID,Lee Taek^ORCID,Kim Min-Gwan^ORCID,In Hoh Peter^ORCID,Kim Leen^ORCID,Lee Heon-Jeong^ORCID

Publisher

JMIR Publications Inc.

Subject

Health Informatics

Reference44 articles.

1. 20082019-04-12World Health OrganizationGenevaThe Global Burden of Disease 2004 Update https://www.who.int/healthinfo/global_burden_disease/2004_report_update/en/

2. Social and economic burden of mood disorders

3. Chronobiology of mood disorders

4. Circadian genes, rhythms and the biology of mood disorders

5. Why Do Mania and Suicide Occur Most Often in the Spring?

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