|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||Abstract:||
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 . 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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