TY - JOUR
T1 - A stochastic model of susceptibility to antibiotic therapy-The effects of cross-resistance and treatment history
AU - Zalounina, Alina
AU - Paul, Mical
AU - Leibovici, Leonard
AU - Andreassen, Steen
PY - 2007/5
Y1 - 2007/5
N2 - Objective: Selection of antibiotic therapy is a complicated process, depending on, among others, the effect of cross-resistance between antibiotics. We propose a model, which incorporates information about treatment history in the form of information on the success or failure of the current treatment and which combines this with data on cross-resistance to predict the susceptibility to future antibiotic treatments, thus providing a systematic basis for revision of antibiotic treatment. Methods and material: The stochastic model was built as a causal probabilistic network (CPN). Data used in the model were based on a bacteriology database including data on patient and episode unique pathogens cultured from a microbiological sample. Results: In this paper, we develop a CPN that can exploit knowledge about cross-resistance between two consecutive treatments, explore the properties of this CPN and consider how the CPN can be integrated into a complete decision support system for selection of antibiotic therapy. Conclusion: The model presented may be useful both as a theoretical tool describing cross-resistance between antibiotics and as a part of complete decision support system for selection of antibiotic therapy.
AB - Objective: Selection of antibiotic therapy is a complicated process, depending on, among others, the effect of cross-resistance between antibiotics. We propose a model, which incorporates information about treatment history in the form of information on the success or failure of the current treatment and which combines this with data on cross-resistance to predict the susceptibility to future antibiotic treatments, thus providing a systematic basis for revision of antibiotic treatment. Methods and material: The stochastic model was built as a causal probabilistic network (CPN). Data used in the model were based on a bacteriology database including data on patient and episode unique pathogens cultured from a microbiological sample. Results: In this paper, we develop a CPN that can exploit knowledge about cross-resistance between two consecutive treatments, explore the properties of this CPN and consider how the CPN can be integrated into a complete decision support system for selection of antibiotic therapy. Conclusion: The model presented may be useful both as a theoretical tool describing cross-resistance between antibiotics and as a part of complete decision support system for selection of antibiotic therapy.
KW - Antibiotic therapy
KW - Bacterial infections
KW - Causal probabilistic networks
KW - Cross-resistance
UR - http://www.scopus.com/inward/record.url?scp=34247630202&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2006.12.007
DO - 10.1016/j.artmed.2006.12.007
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C2 - 17317122
AN - SCOPUS:34247630202
VL - 40
SP - 57
EP - 63
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
SN - 0933-3657
IS - 1
ER -