Robust comparative evaluation of 15 natural language processing algorithms to positively identify patients with inflammatory bowel disease from secondary care records.

ABSTRACT:

Objective

Natural language processing (NLP) can identify cohorts of patients with inflammatory bowel disease (IBD) from free text. However, limited sharing of code, models, and data sets continues to hinder progress. The aim of this study was to evaluate multiple open-source NLP models for identifying IBD cohorts, reporting on document-to-patient-level classification, while exploring explainability, generalisability, fairness and cost.

Methods

15 algorithms were assessed, covering all types of NLP spanning over 50 years of NLP development. Rule-based (regular expressions, spaCy with negation), and vector-based (bag-of-words (BoW), term frequency inverse document frequency (TF IDF), word-2-vector), to transformers: (two sentence-based sBERT models, three bidirectional encoder

SUBMITTER: Stammers M 

PROVIDER: S-EPMC12517008 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature