Deep Learning Models to Screen Electronic Health Records for Breast and Colorectal Cancer Progression: Performance Evaluation Study.

ABSTRACT:

Background

Cancer progression is an important outcome in cancer research. However, it is frequently documented only in electronic health records (EHRs) as unstructured text, which requires lengthy and costly chart reviews to extract for retrospective studies.

Objective

This study aimed to evaluate the performance of 3 deep learning language models in determining breast and colorectal cancer progression in EHRs.

Methods

EHRs for individuals diagnosed with stage 4 breast or colorectal cancer between 2004 and 2020 in Manitoba, Canada, were extracted. A chart review was conducted to identify cancer progression in each EHR. Data were analyzed with pretrained deep learning language models (Bio+ClinicalBERT, Clinical-BigBird, and Clinical-Longformer). Sensitivity, positive p

SUBMITTER: Lambert P 

PROVIDER: S-EPMC12559821 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature