A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

Journal: Journal of affective disorders

PMID: 39909164

Abstract

BACKGROUND: Lack of widespread use of the Patient Health Questionnaire 9-item (PHQ-9) in clinical practice inhibits measurement of treatment follow-up for patients with major depressive disorder (MDD). This study developed, validated and applied a machine learning model to estimate PHQ-9 scores for MDD patients using relevant notes from electronic medical records (EMR).

Authors

Pedro Alves
Carl D Marci

OM1, Inc., Boston, MA, United States of America; Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America. Electronic address: cmarci@om1.com.
Chandra J Cohen-Stavi

OM1, Inc., Boston, MA, United States of America.
Katelynn Murray Whelan

University College Dublin School of Medicine, Dublin, Ireland.
Costas Boussios

OM1, Inc., Boston, MA, United States of America.

Keywords

Adult Depressive Disorder, Major Electronic Health Records Female Humans Machine Learning Male Middle Aged Patient Health Questionnaire Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (39909164)

A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients.

Abstract

Authors

Keywords

External Resources

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