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RI | Publications | Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics
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Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics
D. Nikovski
EEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 4, July, 2000, pp. 509 - 516.
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| Abstract |
The paper discusses several knowledge engineering techniques for the construction of Bayesian networks for medical diagnostics when the available numerical probabilistic information is incomplete or partially correct. This situation occurs often when epidemiological studies publish only indirect statistics and when significant unmodeled conditional dependence exists in the problem domain. While nothing can replace precise and complete probabilistic information, still a useful diagnostic system can be built with imperfect data by introducing domain-dependent constraints. We propose a solution to the problem of determining the combined influences of several diseases on a single test result from specificity and sensitivity data for individual diseases. We also demonstrate two techniques for dealing with unmodeled conditional dependencies in a diagnostic network. These techniques are discussed in the context of an effort to design a portable device for cardiac diagnosis and monitoring from multimodal signals.
| Notes |
Associated lab/group: Robot Learning Lab
Note: special issue on constructing Bayesian networks
| Text Reference |
D. Nikovski, "Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics," EEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 4, July, 2000, pp. 509 - 516.
| BibTeX Reference |
@article{Nikovski_2000_3154,
author = "Daniel Nikovski",
title = "Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics",
journal = "EEE Transactions on Knowledge and Data Engineering",
month = "July",
year = "2000",
volume = "12",
number = "4",
pages = "509 - 516",
note = "special issue on constructing Bayesian networks"
}