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Title: Artificial neural networks as a universal tools for data analysis and modeling in epidemiology and pharmacoeconomics

Abstract:

he aim of the project is to verify potentia! artificial neural networks application as universal data analysis tools m epidemiology and pharmacoeconomics. Artificial neural networks are numbered among computational intelligence systems. Substantial difference between classical computational machines and those built on the neural paradigm is the latter ability to parallel processmg by set of independent units performing simple opera-tions (summation, function transformation). Redundancy and parallelism of computations are implicating -basic advantages of artificial neural net-works m comparison to classical computers like: robustness for the noise in the data, the ability for adaptation, self-organization. Primarily drug assessment was delimited only to its effectiveness. Nowadays pharmacoeconomics is a third part of drug therapy assessment after effectiveness and safety. Information used for exemplary prediction system construction comes from previous pharmacoeconomics analysis of Non-Small Cell Lung Cancer chemotherapy executed in Pharmacoepidemiology and Pharmacoeconomics Department. Depiction of using neural techniques in large, epidemiological datasets analysis is enabled by use of databases from National Center for Health Statistics (NCHS), Center for Disease Control and Prevention (CDC). Obtained results proofs that mentioned above assessmen ; ts.

Place of publishing:

Kraków

Level of degree:

2 - studia doktoranckie

Degree discipline:

informatyka medyczna

Degree grantor:

Wydział Farmaceutyczny

Promoter:

Brandys, Jerzy

Date issued:

2005

Identifier:

oai:dl.cm-uj.krakow.pl:1247

Call number:

ZB-102790

Language:

pol

Access rights:

nieograniczony

Object collections:

Last modified:

Jun 26, 2023

In our library since:

Nov 21, 2012

Number of object content hits:

805

Number of object content views in PDF format

107

All available object's versions:

http://dl.cm-uj.krakow.pl:8080/publication/1247

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