This paper reviews and critically appraises the application of compartmental models for
generating glycaemic predictions and offering clinical decision support in diabetes care …

One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure
from data. The BN-learning problem is NP-hard, so the standard solution is heuristic search …

Learning the structure of a causal graphical model using both observational and
interventional data is a fundamental problem in many scientific fields. A promising direction …

We show how a generic feature-selection algorithm returning strongly relevant variables can
be turned into a causal structure-learning algorithm. We prove this under the Faithfulness …

A computational scheme for reasoning about dynamic systems using (causal) probabilistic
networks is presented. The scheme is based on the framework of Lauritzen and …

This paper considers a parallel algorithm for Bayesian network structure learning from large
data sets. The parallel algorithm is a variant of the well known PC algorithm. The PC …

A computational system for reasoning about dynamic time-sliced systems using Bayesian
networks is presented. The system, called dHugin, may be viewed as a generalization of the …

A model of carbohydrate metabolism has been implemented as a causal probabilistic
network, allowing explicit representation of uncertainties involved in the prediction of 24-h …

We have previously described a physiological model of glucose—insulin interaction in
insulin-dependent (type 1) diabetes mellitus which has been developed for patient and …

Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive
care units is seen as a clinical challenge. The difficulty in diagnosing ventilator-associated …