Aligning Structured and Unstructured Medical Problems Using UMLS

This paper reports a pilot study to align medical problems in structured and unstructured EHR data using UMLS. A total of 120 medical problems in discharge summaries were extracted using NLP software (MedLEE) and aligned with 87 ICD-9 diagnoses for 19 non-overlapping hospital visits of 4 patients. T...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2010-11, Vol.2010, p.91-95
Main Authors: Carlo, Lorena, Chase, Herbert S, Weng, Chunhua
Format: Article
Language:English
Subjects:
Humans
Information Storage and Retrieval
Natural Language Processing
Pilot Projects
Software
Unified Medical Language System
Online Access:Get full text
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Summary:This paper reports a pilot study to align medical problems in structured and unstructured EHR data using UMLS. A total of 120 medical problems in discharge summaries were extracted using NLP software (MedLEE) and aligned with 87 ICD-9 diagnoses for 19 non-overlapping hospital visits of 4 patients. The alignment accuracy was evaluated by a medical doctor. The average overlap of medical problems between the two data sources obtained by our automatic alignment method was 23.8%, which was about half of the manual review result, 43.56%. We discuss the implications for related research in integrating structured and unstructured EHR data.
ISSN:1559-4076