A neuro-symbolic method for understanding free-text medical evidence. (6th May 2021) - Kang, Tian; Turfah, Ali; Kim, Jaehyun; Perotte, Adler; Weng, Chunhua
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.