Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR

TL;DR

This study demonstrates the use of SemEHR, an NLP tool, to extract physical health comorbidities from clinical notes in mental health records, revealing variable performance across disease types.

Contribution

It introduces an NLP-based method for extracting physical health data from mental health records, addressing data limitations in multimorbidity research.

Findings

NLP performance varies across disease groups (F1 0.601 - 0.954).

High inter-annotator agreement (Cohen's Kappa 0.757).

Effective extraction of physical health conditions from clinical notes.

Abstract

Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions from clinical notes using SemEHR. Data was extracted from Clinical Record Interactive Search (CRIS) system at South London and Maudsley Biomedical Research Centre (SLaM BRC) and the cohort consisted of all individuals who had received a primary or secondary diagnosis of severe mental illness between 2007 and 2018. Three pairs of annotators annotated 2403 documents with an average Cohen's Kappa of 0.757. Results show that the NLP performance varies across different diseases areas (F1 0.601 - 0.954) suggesting that the language patterns or terminologies of different condition groups entail different technical challenges to the same NLP task.