DIAVAL, a Bayesian expert system for echocardiography

Díez, F. J.; Mira, José; Iturralde, E; Zubillaga, S.

doi:10.1016/s0933-3657(97)00384-9

Cited by 82 publications

(44 citation statements)

References 15 publications

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“…Noisy-OR and noisy-MAX gates have proven their worth in many real-life applications (e.g., [5,6,7]). Their foremost advantage is a small number of parameters that are sufficient to specify the entire CPT.…”

Section: Introductionmentioning

confidence: 99%

“…Their foremost advantage is a small number of parameters that are sufficient to specify the entire CPT. This leads to a significant reduction of effort in knowledge elicitation from experts [3,5], improves the quality of distributions learned from data [8], and reduces the spacial and temporal complexity of algorithms for Bayesian networks [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Zagorecki

Drużdżel

2013

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract.One problem faced in knowledge engineering for Bayesian networks is the exponential growth of the number of parameters in their conditional probability tables (CPTs). The most common practical solution is application of the noisy-OR (or their generalization, the noisy-MAX) gates, which take advantage of independence of causal interactions and provide a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, we propose an algorithm that fits a noisy-MAX distribution to an existing CPT and we apply it to search for noisy-MAX gates in three existing practical Bayesian networks. We show that noisy-MAX gate provides a surprisingly good fit for as many as 50% of CPTs in these networks. The importance of this finding is that it provides an empirical justification for the use of the noisy-MAX gate as a powerful knowledge engineering tool.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Zagorecki

Drużdżel

2013

IEEE Trans. Syst. Man Cybern, Syst.

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“…The linear scalability is an advantage over Bayesian systems, which usually do not scale to a large number of clinical trials [14,42,43]. The authors of these systems have reported that the sizes of the underlying networks are superlinear in the number of trials [44,45], and that the training time is superlinear in the network size [21,22].…”

Section: Scalabilitymentioning

confidence: 99%

Selection of patients for clinical trials: an interactive web-based system

Fink

Kokku

Nikiforou

et al. 2004

Artificial Intelligence in Medicine

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The purpose of a clinical trial is to evaluate a new treatment procedure. When medical researchers conduct a trial, they recruit participants with appropriate health problems and medical histories. To select participants, they analyze medical records of the available patients, which has traditionally been a manual procedure.We describe an expert system that helps to select patients for clinical trials. If the available data are insufficient for choosing patients, the system suggests additional medical tests and finds an ordering of the tests that reduces their total cost. Experiments show that the system can increase the number of selected patients. We also present an interface that enables a medical researcher to add clinical trials and selection criteria without the help of a programmer. The addition of a new trial takes ten to twenty minutes, and novice users learn the functionality of the interface in about an hour.

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“…Types upsetting the blood vessels include hit, high blood pressure, and aortic aneurism. Many varieties of cardiovascular diseases contribute to ordinary risk factors such as smoking and fatness, and most can be treated with standard of living changes and drugs [10][11][12][13][14][15][16][17][18][19][20][21]. Cardiovascular disease (CVD) is not one solitary disease.…”

Section: Introductionmentioning

confidence: 99%

Anticipating of Cardiovascular Heart Diseases using Computer based Poly Trees Model

AbdElhamidAbbas¹

2012

IJCA

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Cardiovascular Heart Disease CHD is one of main causes of death. Many signs and 49 disease is associated with CHD. The purpose of this paper is to infer accuracy the Probability of Disease considering all factors and signs. To achieve this goal, the paper uses the concept of structured poly tree and Directed Acyclic Graphical model DAG to predict all the cases that can cause Cardiovascular Heart Disease. Depending on a hypothesis samples contain certain number of persons in the social society, this study identifies the existence of the disease in precise method. The main objective of this paper is to forecast the existence of any CHD diseases between samples of patients due to the signs of diseases appear on patients. This paper suggests the methodology of deduction starts by constructing the probabilistic graphical model of cardiovascular diseases, and then assigns and retrieves data through the nodes of the tree based on the rules of creating a Poly-Tree PT. And then seek to recuperate the configuration of the PT whilst making light of eradicating the necessitate intended for exterior semantics to resolve the way of the undergrowth. The first trend is to confine the advance to nondisintegrate PTs. A causal basin initiates with a multi-parent gather and persists in the route of causal stream to embrace all of the child's offspring and all the through parents of offspring. The results based on system simulation depicts that directed acyclic graphical poly tree model predicts all types of heart diseases accurately and easily in an optimal situation. The benefit achieved in accurate inferring methods is to find a new way helps doctor do their diagnosis well. Inference system is presented to avoid false prediction of CHD. The most significant additional properties of this system is taking into account all diseases and signs points to CHD. General Terms

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DIAVAL, a Bayesian expert system for echocardiography

Cited by 82 publications

References 15 publications

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?

Selection of patients for clinical trials: an interactive web-based system

Anticipating of Cardiovascular Heart Diseases using Computer based Poly Trees Model

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