Development of a Perioperative Medication Suspension Decision Algorithm Based on Bayesian Networks.

2024 IEEE/SICE International Symposium on System Integration (SII)(2024)

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In this study, we developed a perioperative drug suspension decision system using a Bayesian network to estimate the appropriate drug suspension period for antithrombotic drugs in the perioperative period. In the past, physicians relied on a vast amount of information in the guidelines to determine the drug suspension period. However, determining the appropriate suspension period was sometimes difficult when competing thrombotic and bleeding risks were present at the time of guideline reference. The proposed method accumulates expert judgments and builds a Bayesian network model based on these data, which successfully demonstrates the estimation of the drug suspension period even in the presence of competing risks. Additionally, a web-application-based interface was created to visually present causal relationships.
Bayesian Model,Suspension Decision,Risk Of Bleeding,Estimation Period,Vast Amount Of Information,Thrombotic Risk,Antithrombotic Drugs,Bayesian Network Model,Drug Suspension,High Risk,Low Risk,Magnetic Resonance Imaging,Coronary Artery,Support System,Platelet Count,Medical Practice,Pharmacists,Medical Field,Highest Probability,Event B,Probability Table,Coronary Artery Bypass,Medium Risk,Percutaneous Coronary Intervention,Antithrombotic Agents,Visual Network,Balloon Catheter,Anesthetic Techniques,Coronary Artery Bypass Grafting
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