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A neuro-symbolic method for understanding free-text medical evidence

Tian Kang,Ali Turfah,Jaehyun Kim,A. Perotte,C. Weng

Published 2021 in J. Am. Medical Informatics Assoc.

ABSTRACT

Abstract Objective We introduce Medical evidence Dependency (MD)–informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability. Materials and Methods We trained one head in the multi-head self-attention model to attend to the Medical evidence Ddependency (MD) and to pass linguistic and domain knowledge on to later layers (MD informed). This MD-informed attention model was integrated into BioBERT and tested on 2 public machine reading comprehension benchmarks for clinical trial publications: Evidence Inference 2.0 and PubMedQA. We also curated a small set of recently published articles reporting randomized controlled trials on COVID-19 (coronavirus disease 2019) following the Evidence Inference 2.0 guidelines to evaluate the model’s robustness to unseen data. Results The integration of MD-informed attention head improves BioBERT substantially in both benchmark tasks—as large as an increase of +30% in the F1 score—and achieves the new state-of-the-art performance on the Evidence Inference 2.0. It achieves 84% and 82% in overall accuracy and F1 score, respectively, on the unseen COVID-19 data. Conclusions MD-informed attention empowers neural reading comprehension models with interpretability and generalizability via reusable domain knowledge. Its compositionality can benefit any transformer-based architecture for machine reading comprehension of free-text medical evidence.

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