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.