Medical data preprocessing for increased selectivity of diagnosis

• Autorzy: Walczak A. , Paczkowski M.

Identyfikatory

DOI

10.1515/bams-2015-0041

• Języki publikacji: EN

Abstrakty

EN

In this review, we present a framework that will enable us to obtain increased accuracy of computer diagnosis in medical patient checkups. To some extent, a new proposition for medical data analysis has been built based on medical data preprocessing. The result of such preprocessing is transformation of medical data from descriptive, semantic form into parameterized math form. A proper model for digging of hidden medical data properties is presented as well. Exploration of hidden data properties achieved by means of preprocessing creates new possibilities for medical data interpretation. Diagnosis selectivity has been increased by means of parameterized illnesses patterns in medical databases.

Słowa kluczowe

EN

data classification; hidden data properties; medical diagnostics

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2016
• Tom: Vol. 12, no. 1
• Strony: 39--43
• Opis fizyczny: Bibliogr. 4 poz., rys., tab.

Twórcy

autor

Walczak A.

• Department of Cybernetics (WCY), Military University of Technology (WAT), Warsaw, Poland

autor

Paczkowski M.

• Department of Cybernetics (WCY), Military University of Technology (WAT), Warsaw, Poland

Bibliografia

• 1. De Dombal FT, Leaper DJ, Staniland JR, McCann AP, Horrocks JC. Computer-aided diagnosis of acute abdominal pain. Br Med J 1972;2:9–13.
• 2. Kuncheva LI, Whitaker CJ. Measures of diversity in classifier ensembles. Mach Learn 2003;51:181–207.
• 3. Innovative Industry Operating Program, 02.03.03.-00-013/08, Research Program of Polish Ministry of Regional Politics.
• 4. Ameljańczyk A. Multicriteria similarity models for medical diagnostic support algorithms. Bio-Algorithms Med-Syst 2009;9:1–7.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.