Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
A therapy planning architecture that combines decision theory and artificial intelligence techniques
Computers and Biomedical Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Simulation Approaches to General Probabilistic Inference on Belief Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
European research efforts in medical knowledge-based systems
Artificial Intelligence in Medicine
Prediction analysis of a wastewater treatment system using a Bayesian network
Environmental Modelling & Software
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We consider a Bayesian statistical approach to model-based prediction of a future patient's response to a therapy, suitable in a wide range of clinical monitoring applications, especially when the observations made on the pathophysiological process of interest are imprecise and sporadic. Potential areas of application range from the predictive control of drug delivery to the management of chronic diseases. A distinctive characteristic of the proposed method is the capability of learning from a database of past patients, by explicitly modeling inter-subject variability of the unknown model parameters, and at an individual's level, by periodic updating of patient-specific parameter estimates on the basis of the accumulating data. By combining information about the population and information contained in the data of the specific patient we improve patient-specific forecasts. In order to make the proposed methodology operational within knowledge-based systems for patient monitoring, we present a Bayesian network representation of the underlying probabilistic model. Inferences involved in the prediction process can thus be performed via general algorithms for probability propagation on a Bayesian network. As an illustration of the proposed methodology we describe numerical results from an application in the field of cancer therapy.