In recent years, a number of studies of the use of computer programs in diagnosis have been performed. Central to each of these efforts has been the development of an explicit, precisely formulated procedure for diagnosis. Such a development is a prerequisite for computer programs of this type. In general, attention has been focused on models of the inference function of diagnosis, the development of a diagnosis from the given set of clinical signs. Some interesting probabilistic models have been developed which employ Bayes rule, c^ Bayes rule has understandable appeal for use in such a model. First, it permits the use of probabilities in inference, This is preferable to a deterministic approach, because it reflects some of the basic uncer...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic met...
Physicians must frequently combine statistical information on prevalence of diseases and on medical ...
Establishing an accurate diagnosis is crucial in everyday clinical practice. It forms the starting p...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
Statistical pattern-recognition techniques have been frequently applied to the problem of medical di...
Coronary heart disease is a heart disease that involves disorders of the blood vessels (coronary art...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
This thesis presents DAMOCLES, a quantitative modelling approach to medical diagnosis that addresses...
Medical diagnosis has been traditionally recognized as a privileged field of application for so call...
Clinical diagnosis is often a complex task of decision making in the face of uncertainty. Diagnosis ...
Medical diagnosis has been traditionally recognized as a privileged field of application for so call...
AbstractModel-based diagnosis concerns using a model of the structure and behaviour of a system or d...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
The extensive research on computer-based medical diagnosis has not had much impact on medical practi...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic met...
Physicians must frequently combine statistical information on prevalence of diseases and on medical ...
Establishing an accurate diagnosis is crucial in everyday clinical practice. It forms the starting p...
Computer-based diagnostic decision support systems (DSS) will play an increasingly important role in...
Statistical pattern-recognition techniques have been frequently applied to the problem of medical di...
Coronary heart disease is a heart disease that involves disorders of the blood vessels (coronary art...
We review recent developments in applying Bayesian probabilistic and statistical ideas to expert sys...
This thesis presents DAMOCLES, a quantitative modelling approach to medical diagnosis that addresses...
Medical diagnosis has been traditionally recognized as a privileged field of application for so call...
Clinical diagnosis is often a complex task of decision making in the face of uncertainty. Diagnosis ...
Medical diagnosis has been traditionally recognized as a privileged field of application for so call...
AbstractModel-based diagnosis concerns using a model of the structure and behaviour of a system or d...
Bayesian networks have proven their value in solving complex diagnostic problems. The main bottlenec...
The extensive research on computer-based medical diagnosis has not had much impact on medical practi...
The paper presents a casual-probabilistic approach to the technical diagnosis in which the solution ...
AbstractThe article presents the main bases of artificial intelligence, probabilistic diagnostic met...
Physicians must frequently combine statistical information on prevalence of diseases and on medical ...