TITLE: Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset 

ABSTRACT: Machine reading comprehension has made great progress in recent years owing
to large-scale annotated datasets. In the clinical domain, however, creating
such datasets is quite difficult due to the domain expertise required for
annotation. Recently, Pampari et al. (EMNLP'18) tackled this issue by using
expert-annotated question templates and existing i2b2 annotations to create
emrQA, the first large-scale dataset for question answering (QA) based on
clinical notes. In this paper, we provide an in-depth analysis of this dataset
and the clinical reading comprehension (CliniRC) task. From our qualitative
analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA
questions are often answerable without using domain knowledge. From our
quantitative experiments, surprising results include that (iii) using a small
sampled subset (5%-20%), we can obtain roughly equal performance compared to
the model trained on the entire dataset, (iv) this performance is close to
human expert's performance, and (v) BERT models do not beat the best performing
base model. Following our analysis of the emrQA, we further explore two desired
aspects of CliniRC systems: the ability to utilize clinical domain knowledge
and to generalize to unseen questions and contexts. We argue that both should
be considered when creating future datasets.
Machine reading comprehension has made great progress in recent years owing
to large-scale annotated datasets. In the clinical domain, however, creating
such datasets is quite difficult due to the domain expertise required for
annotation. Recently, Pampari et al. (EMNLP'18) tackled this issue by using
expert-annotated question templates and existing i2b2 annotations to create
emrQA, the first large-scale dataset for question answering (QA) based on
clinical notes. In this paper, we provide an in-depth analysis of this dataset
and the clinical reading comprehension (CliniRC) task. From our qualitative
analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA
questions are often answerable without using domain knowledge. From our
quantitative experiments, surprising results include that (iii) using a small
sampled subset (5%-20%), we can obtain roughly equal performance compared to
the model trained on the entire dataset, (iv) this performance is close to
human expert's performance, and (v) BERT models do not beat the best performing
base model. Following our analysis of the emrQA, we further explore two desired
aspects of CliniRC systems: the ability to utilize clinical domain knowledge
and to generalize to unseen questions and contexts. We argue that both should
be considered when creating future datasets.
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
http://arxiv.org/abs/2005.00574v1