Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.

ABSTRACT:

Background

Using novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis.

Method

The study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell's C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, p

SUBMITTER: Irving J 

PROVIDER: S-EPMC7965059 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature