Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records.

ABSTRACT:

Background

Self-harm occurring within pregnancy and the postnatal year ("perinatal self-harm") is a clinically important yet under-researched topic. Current research likely under-estimates prevalence due to methodological limitations. Electronic healthcare records (EHRs) provide a source of clinically rich data on perinatal self-harm.

Aims

(1) To create a Natural Language Processing (NLP) tool that can, with acceptable precision and recall, identify mentions of acts of perinatal self-harm within EHRs. (2) To use this tool to identify service-users who have self-harmed perinatally, based on their EHRs.

Methods

We used the Clinical Record Interactive Search system to extract de-identified EHRs of secondary mental healthcare service-users at South London and Maudsley NHS

SUBMITTER: Ayre K 

PROVIDER: S-EPMC8336818 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature