Work

Characterization of patients who suffer asthma exacerbations using data extracted from electronic medical records.

Year: 2008

Type: article

Abstract: The increasing availability of electronic medical records offers opportunities to better characterize patient populations and create predictive tools to individualize health care. We determined which asthma patients suffer exacerbations using data extracted from electronic medical records of the Partners Healthcare System using Natural Language Processing tools from the "Informatics for Integrating Biology to the Bedside" center (i2b2). Univariable and multivariable analysis of data for 11,356 patients (1,394 cases, 9,962 controls) found that race, BMI, smoking history, and age at initial observation are predictors of asthma exacerbations. The area under the receiver operating characteristic curve (AUROC) corresponding to prediction of exacerbations in an independent group of 1,436 asthma patients (106 cases, 1,330 controls) is 0.67. Our findings are consistent with previous characterizations of asthma patients in epidemiological studies, and demonstrate that data extracted by natural language processing from electronic medical records is suitable for the characterization of patient populations.

Source: Europe PMC (PubMed Central)

Authors: Blanca E Himes, Isaac S Kohane, Marco F Ramoni, Scott T Weiss

Institution: Harvard–MIT Division of Health Sciences and Technology

Language: English

Topic: Asthma and respiratory diseases

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, U.S. National Library of Medicine

Awards: Informatics for Integrating Biology & the Bedside (i2b2), Pharmacogenetics of Asthma Treatment, Biomedical Informatics and Data Science Research Training Program (BIRT)