Task 2: Information extraction from Clinical Text: Disease/Disorder Template Filling
To support the continuum of care, our goal is to develop annotated data, resources, methods that make clinical documents easier to understand from nurses and patients’ perspective. Similar to ShARe corpus from ShARe/CLEFeHealth2013 Tasks 1 and 2, we open the lab for method and resource submissions to be evaluated statistically. We will extend Task 1 from 2013 by focusing this year's task on Disease/Disorder Template Filling. For this task, participants will be provided an empty template for each disease/disorder mention; each template consists of the mention's Unified Medical Language System concept unique identifiers (CUI), mention boundaries, and unfilled attribute: value slots. Participants are asked to develop attribute classifiers that predict the value for each each attribute:value slot for the provided disease/disorder mention.
Disease/Disorder Templates consist of 10 different attributes: Negation Indicator, Subject Class, Uncertainty Indicator, Course Class, Severity Class, Conditional Class, Generic Class, Body Location, DocTime Class, and Temporal Expression. There are two attribute: value slot types: normalization and cue. The ShARe/CLEFeHealth2013 Task 1 & 2 corpus and Disease/Disorder Template annotations in English will serve as an initial development set (n=300 documents of 4 clinical report types) and new annotations will be developed to create an unseen evaluation set (n=133 discharge summaries).