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On-time clinical phenotype prediction
based on narrative reports
January 1, 2013

Download Document

BibTex
Authors

Cosmin A. Bejan

Lucy Vanderwende

Heather L. Evans

Mark M. Wurfel

Meliha Yetisgen-Yildiz
Published In

Proceedings of the American Medical Informatics Association Fall Symposium (AMIA'13),
Distinguished Paper Award
Publication Type
Proceedings
Book Title
Proceedings of the American Medical Informatics Association Fall Symposium (AMIA'13),
Distinguished Paper Award
Publisher
American Medical Informatics Association

Abstract

Related Info
Abstract
In this paper we describe a natural language processing system which is able to predict whether
or not a patient exhibits a specific phenotype using the information extracted from the narrative
reports associated with the patient. Furthermore, the phenotypic annotations from our report
dataset were performed at the report level which allows us to perform the prediction of the
clinical phenotype at any point in time during the patient hospitalization period. Our experiments
indicate that an important factor in achieving better results for this problem is to determine how
much information to extract from the patient reports in the time interval between the patient
admission time and the current prediction time.
Related Info
Research Areas
 Natural language processing and speech
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