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血液透析感染风险评估有助于提高血透质量以及保证患者安全。血液透析感染风险受大量不确定性、多状态因素的影响而呈现出动态性,传统故障树分析(FTA)的确定性和二态性使其在评估该类风险时存在缺陷。通过将血液透析感染FTA静态模型映射为Bayesian网络对血液透析感染风险中存在的不确定性关系和多状态变量进行建模,利用Bayesian定理进行概率更新从不同角度识别易导致感染的关键事件,并且考虑患者异质性实现血液透析感染风险的动态评估。通过实例验证了该方法的可行性和有效性,与单一FTA方法相比该方法能够从多个角度对风险进行全面地分析与评估。
Risk assessment of hemodialysis infection can help improve hemodialysis quality and ensure patient safety. The risk of hemodialysis infection is dynamic due to a large number of uncertainties and multi-state factors. The certainty and duality of traditional fault tree analysis (FTA) make it have defects in assessing such risks. By mapping the hemodialysis-infected FTA static model to the Bayesian network for modeling the uncertainties and multi-state variables in the risk of hemodialysis infection, Bayesian theorem was used to update the probability to identify the key events that led to the infection easily. Dynamic assessment of hemodialysis infection risk considering patient heterogeneity. The feasibility and effectiveness of this method are verified by examples. Compared with the single FTA method, this method can comprehensively analyze and evaluate the risks from multiple perspectives.