Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.

ABSTRACT:

Background

Individualising mental healthcare at times when a patient is most at risk of suicide involves shifting research emphasis from static risk factors to those that may be modifiable with interventions. Currently, risk assessment is based on a range of extensively reported stable risk factors, but critical to dynamic suicide risk assessment is an understanding of each individual patient's health trajectory over time. The use of electronic health records (EHRs) and analysis using machine learning has the potential to accelerate progress in developing early warning indicators.

Setting

EHR data from the South London and Maudsley NHS Foundation Trust (SLaM) which provides secondary mental healthcare for 1.8 million people living in four South London boroughs.

Objectives

SUBMITTER: Dutta R 

PROVIDER: S-EPMC10752595 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature