In this work we present a decision support tool for the calculation of time-dependent survival probability for patients after ventricular assist device implantation. Two different models have been developed, a short term one which predicts survival for the first three months and a long term one that predicts survival for one year after implantation. In order to model the time dependencies between the different time slices of the problem, a dynamic Bayesian network (DBN) approach has been employed. DBNs order to capture the temporal events of the patient disease and the temporal data availability. High accuracy results have been reported for both models. The short and long term DBNs reached an accuracy of 96.97% and 93.55% respectively.
A dynamic Bayesian network approach for time-specific survival probability prediction in patients after ventricular assist device implantation.
2014
Abstract
In this work we present a decision support tool for the calculation of time-dependent survival probability for patients after ventricular assist device implantation. Two different models have been developed, a short term one which predicts survival for the first three months and a long term one that predicts survival for one year after implantation. In order to model the time dependencies between the different time slices of the problem, a dynamic Bayesian network (DBN) approach has been employed. DBNs order to capture the temporal events of the patient disease and the temporal data availability. High accuracy results have been reported for both models. The short and long term DBNs reached an accuracy of 96.97% and 93.55% respectively.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
