Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
This addresses the challenge of limited physical health data in mental health records for researchers and clinicians, but it is incremental as it applies an existing NLP tool to a new cohort.
The study tackled the problem of extracting physical health condition data from clinical notes for individuals with severe mental illness using SemEHR, achieving NLP performance with F1 scores ranging from 0.601 to 0.954 across different disease areas.
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.