Building Bayesian Network Models in Medicine: The MENTOR Experience

Authors:
Subramani Mani;Marco Valtorta;Suzanne McDermott
Affiliations:
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, USA 53201;Department of Computer Science and Engineering, University of South Carolina, Columbia, USA 29208;Department of Family and Preventive Medicine, University of South Carolina, Columbia, USA 29208
Venue:
Applied Intelligence
Year:
2005

Citing 11
Cited 4

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms

Probabilistic reasoning in expert systems: theory and algorithms
Bayesian networks without tears: making Bayesian networks more accessible to the probabilistically unsophisticated

AI Magazine
A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Artificial intelligence: a modern approach

Artificial intelligence: a modern approach
Causality: models, reasoning, and inference

Causality: models, reasoning, and inference
Bayesian Networks and Decision Graphs

Bayesian Networks and Decision Graphs
Introduction to Bayesian Networks

Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems

Probabilistic Networks and Expert Systems
Learning Bayesian Networks

Learning Bayesian Networks
The Bayesian structural EM algorithm

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

Comparing risks of alternative medical diagnosis using Bayesian arguments

Journal of Biomedical Informatics
Inducing decision trees from medical decision processes

KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Comparing Bayesian inference and case-based reasoning as support techniques in the diagnosis of Acute Bacterial Meningitis

Expert Systems with Applications: An International Journal
A robust missing value imputation method for noisy data

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

An experiment in Bayesian model building from a large medical dataset for Mental Retardation is discussed in this paper. We give a step by step description of the practical aspects of building a Bayesian Network from a dataset. We enumerate and briefly describe the tools required, address the problem of missing values in big datasets resulting from incomplete clinical findings and elaborate on our solution to the problem. We advance some reasons why imputation is a more desirable approach for model building than some other ad hoc methods suggested in literature. In our experiment, the initial Bayesian Network is learned from a dataset using a machine learning program called CB. The network structure and the conditional probabilities are then modified under the guidance of a domain expert. We present validation results for the unmodified and modified networks and give some suggestions for improvement of the model.