Medical data preprocessing for increased selectivity of diagnosis
| Autor: | Michal Paczkowski, Andrzej Walczak |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Medical diagnostic General Computer Science Computer science business.industry Data classification Medicine (miscellaneous) Health Informatics Pattern recognition computer.software_genre Biochemistry Genetics and Molecular Biology (miscellaneous) 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Data mining Data pre-processing Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | Bio-Algorithms and Med-Systems. 12:39-43 |
| ISSN: | 1896-530X 1895-9091 |
| DOI: | 10.1515/bams-2015-0041 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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