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

Diagnostic Tools in Mental Health (326) Bipolar Disorders (74) Attention Deficit Disorder (ADD/ADHD) (90) Machine Learning (2074) Mental Health Issues in Adolescence (635)

Published on in Vol 12 (2025)

This is a member publication of University of Pittsburgh

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78163, first published .
Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

Authors of this article:

Rasim S Diler1 Author Orcid Image ;   Farzan Vahedifard1 Author Orcid Image ;   Boris Birmaher1 Author Orcid Image ;   Satish Iyengar1 Author Orcid Image ;   Maria Wolfe1 Author Orcid Image ;   Brianna N Lepore1 Author Orcid Image ;   Mariah Chobany1 Author Orcid Image ;   Halimah Abdul-Waalee1 Author Orcid Image ;   Greeshma Malgireddy1 Author Orcid Image ;   Jonathan A Hart1 Author Orcid Image ;   Michele A Bertocci1 Author Orcid Image
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