title: A knowledge model for the interpretation and visualization of NLP-parsed discharged summaries.
abstract: At our institution, a Natural Language Processing (NLP) tool called MedLEE is used on a daily basis to parse medical texts including complete discharge summaries. MedLEE transforms written text into a generic structured format, which preserves the richness of the underlying natural language expressions by the use of concept modifiers (like change, certainty, degree and status). As a tradeoff, extraction of application-specific medical information is difficult without a clear understanding of how these modifiers combine. We report on a knowledge model for MedLEE modifiers that is helpful for a high level interpretation of NLP data and is used for the generation of two distinct views on NLP-parsed discharge summaries: A  physician view  offering a condensed overview of the severity of patient problems and a  data mining view  featuring binary problem states useful for machine learning.
authors.affiliation: Medical Informatics, Columbia University, New York, NY, USA.
authors.forename: M
authors.initials: M
authors.lastname: Krauthammer
authors.forename: G
authors.initials: G
authors.lastname: Hripcsak
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010351:Patient Discharge; D012720:Severity of Illness Index; D014584:User-Computer Interface