3 research outputs found
Toward a Neural Semantic Parsing System for EHR Question Answering
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.Comment: Accepted at the AMIA Annual Symposium 2022 (10 pages, 5 tables, 1
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Biomedical Question Answering: A Survey of Approaches and Challenges
Automatic Question Answering (QA) has been successfully applied in various
domains such as search engines and chatbots. Biomedical QA (BQA), as an
emerging QA task, enables innovative applications to effectively perceive,
access and understand complex biomedical knowledge. There have been tremendous
developments of BQA in the past two decades, which we classify into 5
distinctive approaches: classic, information retrieval, machine reading
comprehension, knowledge base and question entailment approaches. In this
survey, we introduce available datasets and representative methods of each BQA
approach in detail. Despite the developments, BQA systems are still immature
and rarely used in real-life settings. We identify and characterize several key
challenges in BQA that might lead to this issue, and discuss some potential
future directions to explore.Comment: In submission to ACM Computing Survey