Learn More
In the current paper, the Promedas model for internal medicine , developed by our team, is introduced. The model is based on up-to-date medical knowledge and consists of approximately 2000 diagnoses, 1000 findings and 8600 connections between diagnoses and findings, covering a large part of internal medicine. We show that Belief Propagation (BP) can be(More)
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in health care. Due to the inherent probabilistic nature of medical diagnosis, a DSS should preferably be based on a probabilistic model. In particular Bayesian networks provide a powerful and conceptually transparent formalism for probabilistic modeling. A(More)
In this article we show that traditional Cox survival analysis can be improved upon when supplemented with sensible priors and analysed within a neural Bayesian framework. We demonstrate that the Bayesian method gives more reliable predictions, in particular for relatively small data sets. The obtained posterior (the probability distribution of network(More)
Promedas is a medical patient-specific clinical diagnostic decision support systems based on graphical probabilis-tic models. Promedas aims to improve the quality and efficiency of the diagnostic process, while reducing its costs at the same time. Modern-day medical diagnosis is a very complex process, requiring accurate patient data, a profound(More)
The traditional technique to model survival probabilities is the Cox regression analysis Cox and Oakes, 1984]. Recently, also neural networks have been applied for survival analysis and the prediction of prognosis in cancer treatment Liesttl K, 1994]. The main advantages of the neural network approach are the relative ease with which time dependencies in(More)
  • 1