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

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv articl aim examin whether word list reason appoint effect predict laboratoryverifi influenza case syndrom surveil systemsmethod data collect arm forc health longitudin technolog applic medic record system use algorithm combin impact word within reason appoint depend dbst independ ibst bayesian system use receiv oper characterist curv compar accuraci method process reason appoint current previous list diagnos use depart defens syndrom surveil systemresult examin case result influenza test avail reason appoint averag word standard deviat word differ perform algorithm area curv ibst dbst differ statist signific mcnemar statist p conclus data suggest reason appoint can improv accuraci list diagnos predict laboratoryverifi influenza case studi recommend explor dbst algorithm reason appoint predict like influenza case

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