  Applying methods in natural language processing on electronic health records
(EHR) data is a growing field. Existing corpus and annotation focus on modeling
textual features and relation prediction. However, there is a paucity of
annotated corpus built to model clinical diagnostic thinking, a process
involving text understanding, domain knowledge abstraction and reasoning. This
work introduces a hierarchical annotation schema with three stages to address
clinical text understanding, clinical reasoning, and summarization. We created
an annotated corpus based on an extensive collection of publicly available
daily progress notes, a type of EHR documentation that is collected in time
series in a problem-oriented format. The conventional format for a progress
note follows a Subjective, Objective, Assessment and Plan heading (SOAP). We
also define a new suite of tasks, Progress Note Understanding, with three tasks
utilizing the three annotation stages. The novel suite of tasks was designed to
train and evaluate future NLP models for clinical text understanding, clinical
knowledge representation, inference, and summarization.

Computation and Language;Artificial Intelligence;Computers and Society