Named Clinical Entity Recognition Benchmark

TL;DR

This paper presents a standardized benchmark for evaluating language models on clinical entity recognition, utilizing diverse datasets and the OMOP data model to promote transparency and innovation in healthcare NLP.

Contribution

It introduces a comprehensive benchmarking framework and leaderboard for clinical entity recognition, standardizing datasets and evaluation metrics across medical domains.

Findings

Models show varying performance across different clinical entities.

Standardization via OMOP improves interoperability and comparison.

Benchmarking reveals current limitations and areas for improvement.

Abstract

This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives to support applications like automated coding, clinical trial cohort identification, and clinical decision support. The leaderboard provides a standardized platform for assessing diverse language models, including encoder and decoder architectures, on their ability to identify and classify clinical entities across multiple medical domains. A curated collection of openly available clinical datasets is utilized, encompassing entities such as diseases, symptoms, medications, procedures, and laboratory measurements. Importantly, these entities are standardized according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, ensuring consistency and interoperability across different healthcare systems and datasets, and a comprehensive evaluation of model performance. Performance of models is primarily assessed using the F1-score, and it is complemented by various assessment modes to provide comprehensive insights into model performance. The report also includes a brief analysis of models evaluated to date, highlighting observed trends and limitations. By establishing this benchmarking framework, the leaderboard aims to promote transparency, facilitate comparative analyses, and drive innovation in clinical entity recognition tasks, addressing the need for robust evaluation methods in healthcare NLP.