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Peter Lucas Karin Schurink Marc Bonten Stefan Visscher Using a Bayesian-network Model for the Analysis of Clinical Time-series Data University Medical.

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Presentation on theme: "Peter Lucas Karin Schurink Marc Bonten Stefan Visscher Using a Bayesian-network Model for the Analysis of Clinical Time-series Data University Medical."— Presentation transcript:

1 Peter Lucas Karin Schurink Marc Bonten Stefan Visscher Using a Bayesian-network Model for the Analysis of Clinical Time-series Data University Medical Center Utrecht Radboud University Nijmegen AIME 2005

2 Introduction Time is an essential element in the clinical management of patients as disease processes develop in time. A static Bayesian network was developed previously to support clinicians in the diagnosis and treatment of Ventilator-Associated Pneumonia (VAP) as treating and diagnosing critically ill patients is a challenging task. Using a Bayesian-network Model for the Analysis of Clinical Time-series Data Aim We have investigated whether this static Bayesian network can also be used to analyse the temporal data collected in the ICU to suggest appropriate antimicrobial treatment. AIME 2005

3 pathogen 1 pathogen 2 pathogen 3 pathogen 4 pathogen 5 pathogen 6 pathogen 7 coverage 1 coverage 2 coverage 3 coverage 4 coverage 5 coverage 6 coverage 7 Antibiotic therapy Spectrum very narrow narrow intermediate broad Using a Bayesian-network Model for the Analysis of Clinical Time-series Data AIME 2005

4 Methods A temporal database of records was used. Each record represents data of a mechanically ventilated patient in the ICU during a period of 24 hours. For 157 of these episodes, VAP was diagnosed by two infectious-disease specialists. We consider the period from admission to discharge of the patient as a time series X t, t = 0,…, n p, where t = n p is the time of discharge of patient p. For each record, we collected the output of the Bayesian network, i.e., the best possible antimicrobial treatment, taken into account colonisation data from 3 days before. t = 0 t = n p VAP + treatment H. Influenzae Acinetobacter Enterobacter1P. aeruginosa Using a Bayesian-network Model for the Analysis of Clinical Time-series Data AIME 2005

5 Conclusions and Future Research For 38% of the cases, the antibiotic spectrum was too broad. Further research is needed to improve the therapeutic performance of the Bayesian network Using a Bayesian-network Model for the Analysis of Clinical Time-series Data AIME 2005 Poster ~~~~~~ ~~~~~~ ~~~~~~ ~~~~~~ VAP


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