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We introduce a dataset for evidence/rationale extraction on an extrememulti-label classification task over long medical documents. One such task isComputer-Assisted Coding (CAC) which has improved significantly in recentyears, thanks to advances in machine learning technologies. Yet simplypredicting a set of final codes for a patient encounter is insufficient as CACsystems are required to provide supporting textual evidence to justify thebilling codes. A model able to produce accurate and reliable supportingevidence for each code would be a tremendous benefit. However, a humanannotated code evidence corpus is extremely difficult to create because itrequires specialized knowledge. In this paper, we introduce MDACE, the firstpublicly available code evidence dataset, which is built on a subset of theMIMIC-III clinical records. The dataset -- annotated by professional medicalcoders -- consists of 302 Inpatient charts with 3,934 evidence spans and 52Profee charts with 5,563 evidence spans. We implemented several evidenceextraction methods based on the EffectiveCAN model (Liu et al., 2021) toestablish baseline performance on this dataset. MDACE can be used to evaluatecode evidence extraction methods for CAC systems, as well as the accuracy andinterpretability of deep learning models for multi-label classification. Webelieve that the release of MDACE will greatly improve the understanding andapplication of deep learning technologies for medical coding and documentclassification.

MDACE: MIMIC Documents Annotated with Code Evidence