Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.

ABSTRACT:

Background

The digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung cancer from unstructured secondary-care data.

Methods

Patients aged 40 and above who underwent a chest x-ray (CXR) between 2016 and 2022 were included. ICD-10 and unstructured data were pulled from their electronic health records (EHRs) over the preceding 12 months to the CXR. The unstructured data were processed using named entity recognition to extract symptoms,

SUBMITTER: Houston A 

PROVIDER: S-EPMC11616170 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature