Natural language processing and machine learning of electronic health records for prediction of first-time suicide attempts.

ABSTRACT:

Objective

Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.

Methods

This case-control study included patients aged 10-75 years who were seen between 2007 and 2016 from emergency departments and inpatient units. Cases were first-time suicide attempts from coded diagnosis; controls were randomly selected without suicide attempts regardless of demographics, following a ratio of nine controls per case. Four data-driven ML models were evaluated using 2-year historical

SUBMITTER: Tsui FR 

PROVIDER: S-EPMC7966858 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature