Mining free-text medical records for companion animal enteric syndrome surveillance

R. M. Anholt*, J. Berezowski, I. Jamal, C. Ribble, C. Stephen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)417-422
Number of pages6
JournalPreventive Veterinary Medicine
Volume113
Issue number4
DOIs
Publication statusPrint publication - 1 Mar 2014
Externally publishedYes

Keywords

  • Electronic medical record
  • Enteric syndrome
  • Informatics
  • Surveillance
  • Text-mining
  • Veterinary

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