Bayesian Processing of Context-Dependent Text: Reasons for Appointments Can Improve Detection of Influenza

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 sy...

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Bibliographic Details
Published in:Medical decision making 2012-03, Vol.32 (2), p.E1-E9
Main Authors: Alemi, Farrokh, Torii, Manabu, Atherton, Martin J., Pattie, David C., Cox, Kenneth L.
Format: Article
Language:English
Subjects:
Algorithms
Appointments and Schedules
Artificial Intelligence
Bayes Theorem
Bayesian analysis
Biosurveillance
Cross-Sectional Studies
Data processing
Decision making
Diagnosis, Computer-Assisted
Humans
Influenza
Influenza, Human - diagnosis
Influenza, Human - epidemiology
International Classification of Diseases
Longitudinal Studies
medical records
Medical Records Systems, Computerized
Military Medicine
Natural Language Processing
Odds Ratio
Pandemics
Pattern Recognition, Automated
Reproducibility of Results
ROC Curve
Standard deviation
Statistical analysis
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Summary: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.
ISSN:0272-989X
1552-681X
DOI:10.1177/0272989X12439753