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. 2014 Feb;21(e1):e163-8.
doi: 10.1136/amiajnl-2013-001859. Epub 2013 Nov 7.

Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records

Affiliations

Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records

Brandon K Bellows et al. J Am Med Inform Assoc. 2014 Feb.

Abstract

Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified 1487 BED patients with classification accuracy of 91.8% and sensitivity of 96.2% compared to human review. After applying study inclusion criteria, 525 patients had NLP-identified BED only, 1354 had EDNOS only, and 68 had both BED and EDNOS. Patient characteristics were similar between the groups. This is the first study to use NLP as a method to identify BED patients from EHR data and will allow further epidemiological study of patients with BED in systems with adequate clinical notes.

Keywords: binge eating disorder; eating disorder; electronic health record; natural language processing.

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Figures

Figure 1
Figure 1
Current method of diagnosing EDNOS from structured data compared to NLP method for identifying patients diagnosed with BED. BED, binge eating disorder; EDNOS, eating disorder not otherwise specified; NLP, natural language processing.
Figure 2
Figure 2
Diagram of the natural language processing algorithm.
Figure 3
Figure 3
Attrition summary. AN, anorexia nervosa; BED, binge eating disorder; BN, bulimia nervosa; ED, eating disorder; EDNOS, eating disorder not otherwise specified; CD-9, International Classification of Diseases, 9th edition; NLP, natural language processing; VA, Department of Veterans Affairs.

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