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

Theme Issue (2023): Artificial Intelligence (AI) in Nursing (10) AI applications in genomics and pathology informatics, including ‘Generative AI’ (10) Reviews in Nursing (22) Genomics and Bioinformatics for Clinical Use (36) Depression and Mood Disorders; Suicide Prevention (1445) Artificial Intelligence (1556) Machine Learning (1585)

Published on in Vol 7 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54810, first published .
Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review

Authors of this article:

Brittany Taylor1 Author Orcid Image ;   Mollie Hobensack2 Author Orcid Image ;   Stephanie Niño de Rivera1 Author Orcid Image ;   Yihong Zhao1 Author Orcid Image ;   Ruth Masterson Creber1 Author Orcid Image ;   Kenrick Cato3 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Brittany Taylor   1 , AAS, BA, BS, MPhil ;   Mollie Hobensack   2 , BSN, PhD ;   Stephanie Niño de Rivera   1 , BS ;   Yihong Zhao   1 , PhD ;   Ruth Masterson Creber   1 , MSc, PhD ;   Kenrick Cato   3 , PhD

1 School of Nursing, Columbia University, New York, NY, United States

2 Brookdale Department of Geriatrics and Palliative Care, Icahn School of Medicine, Mount Sinai Health System, New York, NY, United States

3 School of Nursing, University of Pennsylvania, Philadelphia, PA, United States

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