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AMIA Annu Symp Proc. 2013 Nov 16;2013:103-10. eCollection 2013.

On-time clinical phenotype prediction based on narrative reports.

Author information

1
Biomedical Informatics, School of Medicine, Vanderbilt University, Nashville, TN;
2
Microsoft Research, Redmond, WA; ; Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, WA;
3
Department of Surgery, School of Medicine, University of Washington, Seattle, WA;
4
Pulmonary and Critical Care Medicine, School of Medicine, University of Washington, Seattle, WA;
5
Biomedical and Health Informatics, School of Medicine, University of Washington, Seattle, WA; ; Department of Linguistics, University of Washington, Seattle, WA.

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.

PMID:
24551325
PMCID:
PMC3900127
[Indexed for MEDLINE]
Free PMC Article

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