Development of a natural language processing algorithm to extract seizure types and frequencies from the electronic health record.

ABSTRACT:

Objective

To develop a natural language processing (NLP) algorithm to abstract seizure types and frequencies from electronic health records (EHR).

Background

Seizure frequency measurement is an epilepsy quality metric. Yet, abstraction of seizure frequency from the EHR is laborious. We present an NLP algorithm to extract seizure data from unstructured text of clinic notes. Algorithm performance was assessed at two epilepsy centers.

Methods

We developed a rules-based NLP algorithm to recognize terms related to seizures and frequency within the text of an outpatient encounter. Algorithm output (e.g. number of seizures of a particular type within a time interval) was compared to seizure data manually annotated by two expert reviewers ("gold standard"). The algorithm was

SUBMITTER: Decker BM 

PROVIDER: S-EPMC9547963 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature