Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing.

ABSTRACT:

Introduction

Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data.

Methods

We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our

SUBMITTER: Ali SR 

PROVIDER: S-EPMC9683031 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature