Interpretative reading of the antibiogram--a semi-naïve Bayesian approach

Artificial Intelligence in Medicine
Steen AndreassenLeonard Leibovici

Abstract

An antibiogram (ABG) gives the results of in vitro susceptibility tests performed on a pathogen isolated from a culture of a sample taken from blood or other tissues. The institutional cross-ABG consists of the conditional probability of susceptibility for pairs of antimicrobials. This paper explores how interpretative reading of the isolate ABG can be used to replace and improve the prior probabilities stored in the institutional ABG. Probabilities were calculated by both a naïve and semi-naïve Bayesian approaches, both using the ABG for the given isolate and institutional ABGs and cross-ABGs. We assessed an isolate database from an Israeli university hospital with ABGs from 3347 clinically significant blood isolates, where on average 19 antimicrobials were tested for susceptibility, out of 31 antimicrobials in regular use for patient treatment. For each of 14 pathogens or groups of pathogens in the database the average (prior) probability of susceptibility (also called the institutional ABG) and the institutional cross-ABG were calculated. For each isolate, the normalized Brier distance was used as a measure of the distance between susceptibility test results from the isolate ABG and respectively prior probabilities and poste...Continue Reading

References

Sep 26, 2006·The Journal of Antimicrobial Chemotherapy·Mical PaulUNKNOWN TREAT Study Group
Feb 24, 2007·Artificial Intelligence in Medicine·Alina ZalouninaSteen Andreassen
Apr 24, 2009·Methods of Information in Medicine·Steen AndreassenMical Paul

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