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

Formative Evaluation of Digital Health Interventions (2422) Natural Language Processing (693) Electronic Health Records (1058) Depression and Mood Disorders; Suicide Prevention (1364) Clinical Information and Decision Making (1411)

Published on in Vol 6, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34436, first published .
The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records

The Value of Extracting Clinician-Recorded Affect for Advancing Clinical Research on Depression: Proof-of-Concept Study Applying Natural Language Processing to Electronic Health Records

Authors of this article:

Vanessa Panaite1, 2 Author Orcid Image ;   Andrew R Devendorf1, 2 Author Orcid Image ;   Dezon Finch1 Author Orcid Image ;   Lina Bouayad3 Author Orcid Image ;   Stephen L Luther1, 4 Author Orcid Image ;   Susan K Schultz5, 6 Author Orcid Image
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