Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Explanation in Bayesian belief networks
Explanation in Bayesian belief networks
Probabilistic reasoning in decision support systems: from computation to common sense
Probabilistic reasoning in decision support systems: from computation to common sense
An evaluation of explanations of probabilistic inference
Computers and Biomedical Research - Papers presented at the 16th symposium on computer applications in medical care (SCAMC)
Cost-based abduction and MAP explanation
Artificial Intelligence
A tutorial on learning with Bayesian networks
Learning in graphical models
A Probabilistic Framework for Explanation
A Probabilistic Framework for Explanation
A review of explanation methods for Bayesian networks
The Knowledge Engineering Review
Predicting dire outcomes of patients with community acquired pneumonia
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Explanation of Bayesian Networks and Influence Diagrams in Elvira
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We present a novel method for obtaining a concise and mathematically grounded description of multivariate differences between a pair of clinical datasets. Often data collected under similar circumstances reflect fundamentally different patterns. For example, information about patients undergoing similar treatments in different intensive care units (ICUs), or within the same ICU during different periods, may show systematically different outcomes. In such circumstances, the multivariate probability distributions induced by the datasets would differ in selected ways. To capture the probabilistic relationships, we learn a Bayesian network (BN) from the union of the two datasets. We include an indicator variable that represents the dataset from which a given patient record is obtained. We then extract the relevant conditional distributions from the network by finding the conditional probabilities that differ most when conditioning on the indicator variable. Our work is a form of explanation that bears some similarity to previous work on BN explanation; however, while previous work has mostly focused on justifying inference, our work is aimed at explaining multivariate differences between distributions.