A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text

TL;DR

This paper introduces a novel end-to-end neural system for jointly extracting concepts, assertions, and relations from clinical text, significantly outperforming traditional pipeline methods and serving as a strong baseline for future research.

Contribution

It proposes the first joint neural model for simultaneous concept, assertion, and relation extraction in clinical text, addressing limitations of pipeline approaches.

Findings

Joint system outperforms pipeline baseline in F1 scores

Effective use of various embedding techniques enhances performance

Provides a publicly available codebase for future research

Abstract

Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.