Data mining and medical knowledge management: cases and by Petr Berka, Jan Rauch, Djamel Abdelkader Zighed

By Petr Berka, Jan Rauch, Djamel Abdelkader Zighed

The healthcare produces a continuing circulate of knowledge, making a want for deep research of databases via info mining instruments and methods leading to improved clinical examine, prognosis, and remedy.

Data Mining and clinical wisdom administration: instances and functions provides case stories on purposes of varied sleek information mining equipment in different vital parts of medication, masking classical info mining tools, elaborated ways with regards to mining in electroencephalogram and electrocardiogram facts, and techniques regarding mining in genetic information. A optimal source for these focused on facts mining and clinical wisdom administration, this e-book tackles moral concerns regarding cost-sensitive studying in medication and produces theoretical contributions touching on normal difficulties of information, details, wisdom, and ontologies.

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By Petr Berka, Jan Rauch, Djamel Abdelkader Zighed

The healthcare produces a continuing circulate of knowledge, making a want for deep research of databases via info mining instruments and methods leading to improved clinical examine, prognosis, and remedy.

Data Mining and clinical wisdom administration: instances and functions provides case stories on purposes of varied sleek information mining equipment in different vital parts of medication, masking classical info mining tools, elaborated ways with regards to mining in electroencephalogram and electrocardiogram facts, and techniques regarding mining in genetic information. A optimal source for these focused on facts mining and clinical wisdom administration, this e-book tackles moral concerns regarding cost-sensitive studying in medication and produces theoretical contributions touching on normal difficulties of information, details, wisdom, and ontologies.

Show description

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Discrimination information is additive in the sense: I(P1⊗ P2 , Q1 ⊗ Q2) = I(P1, Q1) + I(P2, Q2) Proof. Since the logarithmic function is additive, it holds: I(P1 ⊗ P2 , Q1 ⊗ Q2)= ∑ x1∈X1 ∑ x2 ∈X 2  P1 ( x1 ) P2 ( x2 ) log P1 ( x1 ) P2 ( x2 ) = Q1 ( x1 )Q2 ( x2 ) P2 ( x2 )  P1 ( x1 ) ∑ ∑ P ( x ) P ( x ) log Q ( x ) + log Q ( x )  x1∈X1 x2 ∈X 2 1 1 2 2  P1 ( x1 ) 1 1 2 2  P2 ( x2 ) ∑ P ( x ) log Q ( x ) + ∑ P ( x ) log Q ( x ) = I(P , Q ) + (P , Q ) . ) Example 16. Consider formally the same situation as in Example 12 with p=1/2 and X describing survival of the victims of bomb attacks in the Middle East.

Example 8. The source vector of data in Example 6, or at least its coordinate h, as a complement to the derived vector (1, 1, 1, 0, 0, 0) of Example 3, will become very important if and when a dispute arises about the respective patient’s life insurance, effective at a given time described by vector (y, m, d, h0). Inequalities h > h0 or h < h0 can be decisive with respect to considerable amounts of money then. 3, will not make such decision-making possible, and a loss of source data might cause a costly lawsuit.

Discussions on these issues drew a modern parallel to those between realists and nominalists in classical philosophy. Carnap himself tried, for many years, to prove that this reduction of theoretical terms was possible. However, his effort was in vain and eventually Carnap accepted the opinion, which prevails today, namely, that this reduction is impossible (Carnap, 1937, Carnap, 1950). A prevailing majority of experts, who deal with methodology of science nowadays, think that properties of theoretical terms can be defined with the aid of the 25 Data, Information and Knowledge so-called semantic postulates.

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