DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 1083–1097 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 1083 DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries Jayetri Bardhan1†, Anthony Colas1‡, Kirk Roberts2∗, Daisy Zhe Wang1⋄ 1Department of Computer and Information Science and Engineering, University of Florida 2School of Biomedical Informatics, The University of Texas Health Science Center at Houston {jayetri.bardhan†, acolas1‡, daisyw⋄}@ufl.edu, kirk.roberts@uth.tmc.edu∗ Abstract This paper develops the first question answering dataset (DrugEHRQA) containing question-answer pairs from both structured tables and unstructured notes from a publicly available Electronic Health Record (EHR). EHRs contain patient records, stored in structured tables and unstructured clinical notes. The information in structured and unstructured EHRs is not strictly disjoint: information may be duplicated, contradictory, or provide additional context between these sources. Our dataset has medication-related queries, containing over 70,000 question-answer pairs. To provide a baseline model and help analyze the dataset, we have used a simple model (MultimodalEHRQA) which uses the predictions of a modality selection network to choose between EHR tables and clinical notes to answer the questions. This is used to direct the questions to the table-based or text-based state-of-the-art QA model. In order to address the problem arising from complex, nested queries, this is the first time Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (RAT-SQL) has been used to test the structure of query templates in EHR data. Our goal is to provide a benchmark dataset for multi-modal QA systems, and to open up new avenues of research in improving question answering over EHR structured data by using context from unstructured clinical data. Keywords: Multimodal Question Answering, Electronic Health Records (EHRs), databases, information retrieval 1. Introduction Electronic Health Records (EHRs) are digitized records of patients’ medical history, which can be either in struc- tured or unstructured form. MIMIC-III (Medical Infor- mation Mart for Intensive Care) (Johnson et al., 2016a) is an example of a large EHR database containing health- related information for over 40,000 patients who were admitted in critical care units in the period between 2001 and 2012 in the Beth Israel Deaconess Medical Center. MIMIC-III contains multi-relational tables with patient-specific data on diagnosis, medications, admis- sion, lab tests, etc. Question answering over EHRs aid doctors in diagnosing, while helping patients obtain an- swers to health-related queries. The structured relational database has multiple tables which store information about the patient’s demographics, diagnoses, medica- tions, lab tests along with their results. Conversely, the unstructured data, are notes entered by clinicians that contain a detailed description of every patient’s visit, their past medical history, their problem, symptoms and more. Thus, to benefit from both the modalities, there arises a need for a multi-modal QA dataset on EHRs. We present DrugEHRQA, the first QA dataset which uses both the structured tables and the unstructured clin- ical notes of an EHR to answer questions. The answers from the clinical notes are used to support or provide …

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