Bayesian processing of context-dependent text: reasons for appointments can improve detection of influenza.

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.

DOI: 10.1177/0272989X12439753

Cite this paper

@article{Alemi2012BayesianPO, title={Bayesian processing of context-dependent text: reasons for appointments can improve detection of influenza.}, author={Farrokh Alemi and Manabu Torii and Martin J Atherton and David C Pattie and Kenneth Cox}, journal={Medical decision making : an international journal of the Society for Medical Decision Making}, year={2012}, volume={32 2}, pages={E1-9} }