Information Extraction of Clinical Trial Eligibility Criteria

TL;DR

This paper presents a novel information extraction system for clinical trial eligibility criteria, combining machine learning and grammar-based methods to convert complex free-text into structured, computer-interpretable data, improving eligibility determination.

Contribution

It introduces the first application of attention-based CRF for NER and word2vec clustering for NEL in clinical trial criteria extraction, advancing the state-of-the-art.

Findings

System achieves competitive performance with Criteria2Query.

First use of attention-based CRF for NER in this domain.

Effective combination of machine learning and grammar-based approaches.

Abstract

Clinical trials predicate subject eligibility on a diversity of criteria ranging from patient demographics to food allergies. Trials post their requirements as semantically complex, unstructured free-text. Formalizing trial criteria to a computer-interpretable syntax would facilitate eligibility determination. In this paper, we investigate an information extraction (IE) approach for grounding criteria from trials in ClinicalTrials(dot)gov to a shared knowledge base. We frame the problem as a novel knowledge base population task, and implement a solution combining machine learning and context free grammar. To our knowledge, this work is the first criteria extraction system to apply attention-based conditional random field architecture for named entity recognition (NER), and word2vec embedding clustering for named entity linking (NEL). We release the resources and core components of our system on GitHub at https://github.com/facebookresearch/Clinical-Trial-Parser. Finally, we report our per module and end to end performances; we conclude that our system is competitive with Criteria2Query, which we view as the current state-of-the-art in criteria extraction.