Bayesian Processing of Context-Dependent Text

Farrokh Alemi, Manabu Torii, Martin J. Atherton, David C. Pattie and Kenneth L. Cox

Medical Decision Making, 2012, vol. 32, issue 2, E1-E9

Abstract: Objective . This article aims to examine whether words listed in reasons for appointments could effectively predict laboratory-verified influenza cases in syndromic surveillance systems. Methods . Data were collected from the Armed Forces Health Longitudinal Technological Application medical record system. We used 2 algorithms to combine the impact of words within reasons for appointments: Dependent (DBSt) and Independent (IBSt) Bayesian System. We used receiver operating characteristic curves to compare the accuracy of these 2 methods of processing reasons for appointments against current and previous lists of diagnoses used in the Department of Defense’s syndromic surveillance system. Results . We examined 13,096 cases, where the results of influenza tests were available. Each reason for an appointment had an average of 3.5 words (standard deviation = 2.2 words). There was no difference in performance of the 2 algorithms. The area under the curve for IBSt was 0.58 and for DBSt was 0.56. The difference was not statistically significant (McNemar statistic = 0.0054; P = 0.07). Conclusions . These data suggest that reasons for appointments can improve the accuracy of lists of diagnoses in predicting laboratory-verified influenza cases. This study recommends further exploration of the DBSt algorithm and reasons for appointments in predicting likely influenza cases.

Keywords: Dependent Bayesian System; naive Bayes; text classification; reason for appointment; case definition; influenza (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:32:y:2012:i:2:p:e1-e9

DOI: 10.1177/0272989X12439753

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