Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  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.
['Daniel Nikovski']
I am writing a research paper on a idea Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics
[{"from": "human", "value": "I am writing a research paper on a idea Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  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."}]