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Aligning Structured and Unstructured Medical Problems Using UMLS.

2010; National Institutes of Health; Volume: 2010; Linguagem: Inglês

Lorena Carlo, Herbert Chase, Chunhua Weng,

Topic Modeling

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.