Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

Clinical question answering (QA) (or reading comprehension) aims to
automatically answer questions from medical professionals based on clinical
texts. We release this dataset, which contains 1287 annotated QA pairs on 36
sampled discharge summaries from MIMIC-III Clinical Notes, to facilitate the
clinical question answering task. Questions in our dataset are either verified
or directly generated by clinical experts.

Note that the primary purpose of this dataset is to test the generalizability
of a QA model, i.e., whether a QA model that is trained on other datasets can
answer questions on this dataset (which may have a different distribution
compared with the training data), rather than to train a QA model. Hence the
scale of our annotations is relatively small compared to some existing QA
datasets.