Mining free-text medical records for companion animal enteric syndrome surveillance.
Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals.
Faculty of Veterinary Medicine, Department of Ecosystem and Public Health, University of Calgary, Alberta, Canada T2N 4N1. Electronic address: rmanholt@ucalgary.ca.
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Anholt
Veterinary Public Health-Institute, Vetsuisse Faculty, University of Bern, Schwarzenburgstrasse 155, CH-3097 Bern, Switzerland.
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Berezowski
AQL Management Consulting, Edmonton, Alberta, Canada T6J 2R8.
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Jamal
Faculty of Veterinary Medicine, Department of Ecosystem and Public Health, University of Calgary, Alberta, Canada T2N 4N1; Centre for Coastal Health, Nanaimo, British Columbia, Canada V9R 5S5.
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Ribble
Faculty of Veterinary Medicine, Department of Ecosystem and Public Health, University of Calgary, Alberta, Canada T2N 4N1; Centre for Coastal Health, Nanaimo, British Columbia, Canada V9R 5S5.
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Stephen
D000416:Alberta / Q000453:epidemiology; D000818:Animals; D002371:Cat Diseases / Q000453:epidemiology* / Q000209:etiology; D002415:Cats; D057225:Data Mining; D004283:Dog Diseases / Q000453:epidemiology* / Q000209:etiology; D004285:Dogs; D057286:Electronic Health Records; D062665:Epidemiological Monitoring / Q000662:veterinary*; D005767:Gastrointestinal Diseases / Q000453:epidemiology / Q000209:etiology / Q000662:veterinary*; D015995:Prevalence; D012680:Sensitivity and Specificity
Electronic medical record; Enteric syndrome; Informatics; Surveillance; Text-mining; Veterinary