Depression screening using mobile phone usage metadata: a machine learning approach.

Journal: Journal of the American Medical Informatics Association : JAMIA

Published Date: Apr 1, 2020

Abstract

OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, this study examines the possibility of screening for depressive symptoms continuously based on patients' mobile usage patterns.

Authors

Rouzbeh Razavi

Department of Management and Information Systems, Kent State University, Kent, OH, USA.
Amin Gharipour

School of Information Technology, Griffith University, Gold Coast, Australia.
Mojgan Gharipour

Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.

Keywords

Adult Algorithms Area Under Curve Cell Phone Use Depression Depressive Disorder Humans Logistic Models Machine Learning Mobile Applications Neural Networks, Computer Sensitivity and Specificity Severity of Illness Index Surveys and Questionnaires Telemedicine

External Resources

View on PubMed Access via DOI PubMed (31977041)

Depression screening using mobile phone usage metadata: a machine learning approach.

Abstract

Authors

Keywords

External Resources

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