Toward a Neural Semantic Parsing System for EHR Question Answering
This work addresses the problem of automating information retrieval from electronic health records for healthcare professionals, but it is incremental as it evaluates existing neural methods rather than introducing new ones.
The paper assessed the performance of two neural semantic parsing models for electronic health record question answering, finding promising results on two clinical datasets with ease of application and generalizability.
Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA). We found that the performance of these advanced neural models on two clinical SP datasets is promising given their ease of application and generalizability. Our error analysis surfaces the common types of errors made by these models and has the potential to inform future research into improving the performance of neural SP models for EHR QA.