Accepted for/Published in: JMIR Formative Research
Date Submitted: Oct 22, 2021
Date Accepted: Jan 26, 2022
The value of extracting clinician recorded affect for advancing depression clinical research: A proof of concept applying NLP to electronic health records and future directions
ABSTRACT
Background:
Affective characteristics are associated with depression severity, course, and outcomes.
Objective:
In the current paper we propose an information extraction vocabulary used to pilot the feasibility and reliability of identifying clinician recorded patient affective states in clinical notes from electronic health records (EHRs).
Methods:
Affect and mood were annotated in 149 clinical notes of 109 patients by two independent coders across three pilots. Intercoder discrepancies were settled by a third coder. This reference annotation set was used to test a proof of concept NLP system using a Named Entity Recognition approach.
Results:
Concepts were frequently addressed in templated format and free text in clinical notes. Annotated data demonstrated that affective characteristics were identified in 88% of the notes; mood was identified in 97% of the notes. The intercoder reliability was consistently good across the pilots (Inter-Annotator Agreement (IAA) > 70%). The final NLP system showed good reliability with the final reference annotation set: mood IAA = 85.8%; affect IAA = 80.9%.
Conclusions:
Affect and mood can be reliably identified in clinician reports and are good targets for NLP. We discuss several next steps to expand on this proof of concept and the value of this research for depression clinical research.
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Copyright
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