Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists

Adrienne Grzenda, Nina V. Kraguljac, William M. McDonald, Charles Nemeroff, John Torous, Jonathan E. Alpert, Carolyn I. Rodriguez, Alik S. Widge

Research output: Contribution to journalReview articlepeer-review

26 Scopus citations


“Mr. A,” a 24-year-old man, presents for evaluation of worsening depression. He describes a history of depression since adolescence, although he notes that he suffered a troubled childhood, including emotional neglect. He believes a recent breakup and having been denied a promotion precipitated this episode. “I’m sleeping all the time, and my body feels heavy,” he adds. He also reports increased appetite, weight gain, and “urges to cut, which I have not done in years.” However, he remains social and actively involved in several hobbies. He discontinued bupropion and escitalopram in the past because of “terrible headaches and irritability.” Initially, you consider starting lamotrigine. However, your office recently implemented a clinical decision support system that recommends a trial of phenelzine. The patient’s symptoms remit entirely on the medication suggested by the system. Curious as to how the system decided on this treatment, you download several papers on its development.

Original languageEnglish (US)
Pages (from-to)715-729
Number of pages15
JournalAmerican Journal of Psychiatry
Issue number8
StatePublished - Aug 2021

ASJC Scopus subject areas

  • Psychiatry and Mental health


Dive into the research topics of 'Evaluating the Machine Learning Literature: A Primer and User’s Guide for Psychiatrists'. Together they form a unique fingerprint.

Cite this