A continuous time Bayesian network model for cardiogenic heart failure
作者: E. GattiD. LucianiF. Stella
作者单位: 1DISCo, Università degli Studi di Milano-Bicocca
2Laboratorio di Epidemiologia Clinica, Istituto di Ricerche Farmacologiche Mario Negri
刊名: Flexible Services and Manufacturing Journal, 2012, Vol.24 (4), pp.496-515
来源数据库: Springer Nature Journal
DOI: 10.1007/s10696-011-9131-2
关键词: Cardiogenic heart failureContinuous time Bayesian networksDecision support system
英文摘要: Abstract(#br)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.
全文获取路径: Springer Nature  (合作)
影响因子:0.857 (2012)

  • cardiogenic 心原性
  • failure 破坏
  • heart 心脏
  • continuous 连续的
  • network 网络
  • model 模型