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

Electronic Health Records (EHR) contain patient records, stored in structured
tables as well as unstructured clinical notes. The information in structured
and unstructured EHR records is not strictly disjoint: information may be
duplicated, contradictory, or provide additional context between these
sources. This presents a rich opportunity to study question answering (QA)
models that combine reasoning over both structured and unstructured data. This
work presents the first question answering (QA) dataset (DrugEHRQA) containing
question-answer pairs from both structured tables and unstructured notes from
MIMIC-III, a publicly available Electronic Health Record (EHR). We are
releasing a QA dataset over MIMIC-III tables through PhysioNet, containing
41,417 triplets of natural language questions, its corresponding SQL query and
the answer retrieved from MIMIC-III tables. We also generated a QA dataset on
the unstructured clinical notes of MIMIC-III which can be found in the n2c2
repository. Both these datasets are combined to generate a multimodal QA
dataset (DrugEHRQA), which contains question-answers from both structured and
unstructured data of MIMIC-III. The DrugEHRQA dataset has medication-related
queries, containing over 70,000 question-answer pairs. 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.