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

Applications of AI (272) Chronic Conditions (422) Research Methodology - AI (33) Machine Learning (2034) Mental Health Surveillance and Epidemiology (93) Methods and New Tools in Mental Health Research (547) Prevention and Health Promotion (1324)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72599, first published .
Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis

Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis

Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis

Authors of this article:

Lizhong Liang1, 2 Author Orcid Image ;   Tianci Liu3 Author Orcid Image ;   William Ollier4 Author Orcid Image ;   Yonghong Peng5 Author Orcid Image ;   Yao Lu1 Author Orcid Image ;   Chao Che3 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Tanoto P, Ye H, Saffari S, Leow Y, Vipin A, Lee F, Ghildiyal S, Liew S, Mohammed A, Sandhu G, Aravindhan K, Bhalla G, Shaik Mohamed Salim R, Kandiah N. Detection of Vascular Mild Cognitive Impairment in Southeast Asia Using the Visual Cognitive Assessment Test: Machine Learning Analysis From the BIOCIS (Biomarkers and Cognition Study, Singapore). JMIR Aging 2025;8:e76847 View