Jan P. Neijt

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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)
In the current paper, the Promedas model for internal medicine, developed by our team, is introduced. The model is based on up-todate 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)
Promedas is a medical patient-specific clinical diagnostic decision support systems based on graphical probabilistic 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)
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)
The traditional technique to model survival probabilities is the Cox regression analysis [Cox and Oakes, 1984]. Recently, also neural networks have been ap­ plied for survival analysis and the prediction of prognosis in cancer treatment [Liest01 K, 1994]. The main advantages of the neural network approach are the relative ease with which time dependencies(More)
A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and(More)
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