Analysis of Risk Factor Domains in Psychosis Patient Health Records
This work addresses the costly issue of hospital readmissions for psychiatric patients, but it is incremental as it focuses on preliminary topic extraction without final prediction outcomes.
The authors tackled the problem of predicting psychiatric patient readmissions by developing a topic extraction pipeline from electronic health records, showing initial results for their model and outlining future feature additions.
Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.