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Issue No.04 - July/August (2000 vol.12)
pp: 517-528
ABSTRACT
<p><b>Abstract</b>—The fatality rate associated with severe bacterial infections is about 30 percent and appropriate antibiotic treatment reduces it by half. Unfortunately, about a third of antibiotic treatments prescribed by physicians are inappropriate. We have built a causal probabilistic network (CPN) for treatment of severe bacterial infections. The net is based on modules, each module representing a site of infection. The general configuration of a module is as follows: Major distribution factors define groups of patients, each of them with a definite prevalence of infection caused by a given pathogen. Minor distribution factors multiply the likelihood of one pathogen, without changing much of the prevalence of infection. Infection caused by a pathogen causes local and generalized signs and symptoms. Antibiotic treatment is appropriate if it matches the susceptibility of the pathogens in vitro and appropriate treatment is associated with a gain in life expectancy. This is balanced against the cost of the drug, side effects, and ecological damage, to reach the most cost effective treatment. The net was constructed in such a way that the data for the conditional probability tables will be available, even if it meant sometimes giving up on fine modeling details. For data, we used large databases collected by us in the last 10 years and data from the literature. The CPN was a convenient way to combine data from databases collected at different locations and times with published information. Although the net is based on detailed and large databases, its calibration to new sites requires data that is available in most modern hospitals.</p>
INDEX TERMS
Causal probabilistic networks, bacterial infections, bacteremia, antibiotic treatment, cost-effectiveness.
CITATION
Leonard Leibovici, Michal Fishman, Henrik C. Schønheyder, Christian Riekehr, Brian Kristensen, Ilana Shraga, Steen Andreassen, "A Causal Probabilistic Network for Optimal Treatment of Bacterial Infections", IEEE Transactions on Knowledge & Data Engineering, vol.12, no. 4, pp. 517-528, July/August 2000, doi:10.1109/69.868905
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