Publication details
Machine Learning–Based Knowledge Extraction from Complex Clinical Oncological Data
| Authors | |
|---|---|
| Year of publication | 2006 |
| Type | Article in Proceedings |
| Conference | Knowledge Extraction and Modeling KNEMO-2006 |
| MU Faculty or unit | |
| Citation | |
| Field | Informatics |
| Keywords | machine learning knowledge extraction oncology complex data |
| Description | The article describes some bio–informatics problems and results achieved by application of selected machine–learning tools to extracting knowledge from relatively difficult clinical oncological data. The structure of the clinical data allows detailed analyses of epidemiological and clinical aspects. Performed analyses can provide significant predictions not only for diagnostic risk factors but also for the applied therapeutic strategy. Despite the complexity and issues of the particular acute–leukemia data, experimental results demonstrated good applicability of the tools (as the automatically generated decision trees and rules) to certain difficult problems with predictions, or looking for relevant attributes. Naturally, many problems are still waiting for solutions. |