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
Article Authors Cited by (2) Tweetations (5) Metrics

Journals

  1. Li N, Feng L, Hu J, Jiang L, Wang J, Han J, Gan L, He Z, Wang G. Using deeply time-series semantics to assess depressive symptoms based on clinical interview speech. Frontiers in Psychiatry 2023;14 View
  2. Panaite V, Finch D, Pfeiffer P, Cohen N, Alman A, Haun J, Schultz S, Miles S, Belanger H, Kozel F, Rottenberg J, Devendorf A, Barrett B, Luther S. Predictive modeling of initiation and delayed mental health contact for depression. BMC Health Services Research 2024;24(1) View