tetano
Editor, Senior Moderator
Med Decis Making. 2012 Mar 16. [Epub ahead of print]
Bayesian Processing of Context-Dependent Text: Reasons for Appointments Can Improve Detection of Influenza.
Alemi F, Torii M, Atherton MJ, Patii DC, Cox KL.
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.
PMID:
22427368
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22427368
Bayesian Processing of Context-Dependent Text: Reasons for Appointments Can Improve Detection of Influenza.
Alemi F, Torii M, Atherton MJ, Patii DC, Cox KL.
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.
PMID:
22427368
[PubMed - as supplied by publisher]
http://www.ncbi.nlm.nih.gov/pubmed/22427368