Tasks 1 and 3 from Progress Note Understanding Suite of Tasks: SOAP Note Tagging and Problem List Summarization

Applying methods in natural language processing on electronic health records
(EHR) data is a growing field. Existing corpus and annotation focus on
modelling textual features and relation prediction [1] . However, there is a
paucity of annotated corpus built to model clinical diagnostic reasoning, a
process that involves text understanding, domain knowledge abstraction and
reasoning, and clinical text generation. The datasets here support a
hierarchical annotation schema with two out of the three stages available to
address clinical text understanding and text generation. The datasets provided
here are for individual tasks in Stages 1 and 3. The task for Stage 2 was
previously accepted as part of the National NLP Clinical Challenges (n2c2) and
may be retrieved from the n2c2 challenge website.

The annotated corpus is based on an extensive collection of Intensive Care
Unit progress notes, a type of EHR documentation that is collected in time
series in a problem-oriented format. The progress notes were sourced from
MIMIC-III. The conventional format for a progress note follows a Subjective,
Objective, Assessment and Plan heading (SOAP). The novel suite of tasks was
designed to train and evaluate future NLP models for clinical text
understanding, clinical knowledge representation, inference, and
summarization. The ultimate goal of these datasets is to advance the
development and evaluation of NLP models for clinical applications that lead
to AI-assisted clinical decision support and reduce medical errors.