Authors: Perović, Aleksandar
Doder, Dragan
Ognjanović, Zoran 
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Applications of probabilistic and related logics to decision support in medicine
First page: 35
Last page: 77
Related Publication(s): Computational Medicine in Data Mining and Modeling
Issue Date: 1-Dec-2013
Rank: M14
ISBN: 978-1-4614-8785-2
DOI: 10.1007/978-1-4614-8785-2_2
Since the late 60s, probability theory has found application in development of various medical expert systems. Bayesian analysis, which is essentially an optimal path finding through a graph called Bayesian network, has been (and still is) successfully applied in so-called sequential diagnostics, when the large amount of reliable relevant data is available. The graph (network) represents our knowledge about connections between studied medical entities (symptoms, signs, diseases); the Bayes formula is applied in order to find the path (connection) with maximal conditional probability. Moreover, a priori and conditional probabilities were used to define a number of measures designed specifically to handle uncertainty, vague notions, and imprecise knowledge. Some of those measures were implemented in MYCIN in the early 70s [96]. The success of MYCIN has initiated construction of rule-based expert systems in various fields.
Keywords: Bayesian Analysis | Conditional probabilities | Decision support in medicine | Imprecise knowledge | Medical expert system | Optimal path findings | Probability theory | Rule based expert systems
Publisher: Springer Link

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