Deep Learning and Geriatric Mental Health

Authors

Howard Aizenstein, Department of Psychiatry (HA), University of Pittsburgh School of Medicine, Pittsburgh, PA. Electronic address: aizen@pitt.edu.
Raeanne C. Moore, Department of Psychiatry (RCM), University of California San Diego, San Diego, CA.
Ipsit Vahia, Division of Geriatric Psychiatry (IV), Harvard Medical School, Boston, MA.
Adam Ciarleglio, Department of Biostatistics and Bioinformatics (AC), George Washington University, Washington, D.C.

Document Type

Journal Article

Publication Date

12-5-2023

Journal

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry

DOI

10.1016/j.jagp.2023.11.008

Keywords

Deep learning; geriatric mental health; machine learning; neuroimaging

Abstract

The goal of this overview is to help clinicians develop basic proficiency with the terminology of deep learning and understand its fundamentals and early applications. We describe what machine learning and deep learning represent and explain the underlying data science principles. We also review current promising applications and identify ethical issues that bear consideration. Deep Learning is a new type of machine learning that is remarkably good at finding patterns in data, and in some cases generating realistic new data. We provide insights into how deep learning works and discuss its relevance to geriatric psychiatry.

APA Citation

Aizenstein, Howard; Moore, Raeanne C.; Vahia, Ipsit; and Ciarleglio, Adam, "Deep Learning and Geriatric Mental Health" (2023). GW Authored Works. Paper 4004.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/4004

Department

Biostatistics and Bioinformatics