title: Aligning Structured and Unstructured Medical Problems Using UMLS.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, 10032.
authors.forename: Lorena
authors.initials: L
authors.lastname: Carlo
authors.forename: Herbert S
authors.initials: HS
authors.lastname: Chase
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D006801:Humans; D016247:Information Storage and Retrieval; D009323:Natural Language Processing; D010865:Pilot Projects; D012984:Software; D017432:Unified Medical Language System