A continuous time Bayesian network model for cardiogenic heart failure
A continuous time Bayesian network model for cardiogenic heart failure
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DOI：
10.1007/s10696-011-9131-2
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发表时间：
2012-12-01
影响因子：
2.7
通讯作者：
Stella, F.
中科院分区：
文献类型：
--
作者：
Gatti, E.;Luciani, D.;Stella, F.
连续时间贝叶斯网络被用来诊断心源性心力衰竭并预测其可能的演变。该模型克服了动态贝叶斯网络建模和计算的局限性。它既包括不可观察的生理变量，也包括临床和仪器上可观察到的事件，这些事件可能支持心肌梗死和未来休克的诊断。本文介绍了三个与心源性心力衰竭相关的案例研究。该模型根据从患者那里收集的证据的变化来预测复杂疾病的发生和心力衰竭的持续性。预测结果显示与目前对临床图像的病理生理医学理解是一致的。
Continuous time Bayesian networks are used to diagnose cardiogenic heart failure and to anticipate its likely evolution. The proposed model overcomes the strong modeling and computational limitations of dynamic Bayesian networks. It consists of both unobservable physiological variables, and clinically and instrumentally observable events which might support diagnosis like myocardial infarction and the future occurrence of shock. Three case studies related to cardiogenic heart failure are presented. The model predicts the occurrence of complicating diseases and the persistence of heart failure according to variations of the evidence gathered from the patient. Predictions are shown to be consistent with current pathophysiological medical understanding of clinical pictures.