Intelligent Medical Decision Support System Based on Imperfect Information [electronic resource] : The Case of Ovarian Tumor Diagnosis /
By: Dyczkowski, Krzysztof [author.].
Contributor(s): SpringerLink (Online service).
Series: Studies in Computational Intelligence: 735Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XXI, 123 p. 55 illus., 51 illus. in color. | Binding - Card Paper |.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319670058.Subject(s): Computer Engineering
| Artificial Intelligence | Biomedical Engineering/Biotechnology | Oncology | Biomedical Engineering and BioengineeringDDC classification: 006.3 Online resources: Click here to access eBook in Springer Nature platform. (Within Campus only.)
In:
Springer Nature eBookSummary: This book discusses computer-supported medical diagnosis with a particular focus on ovarian tumor diagnosis – since ovarian cancer is difficult to diagnose and has high mortality rates, especially in Central and Eastern Europe. It presents the theoretical foundations (both medical and mathematical) of the intelligent OvaExpert system, which supports decision-making in tumor diagnosis. OvaExpert was created primarily to help gynecologists predict the malignancy of ovarian tumors by applying the existing diagnostic models and using modern methods of computational intelligence that accommodate imprecise and imperfect medical data, both of which are common features of everyday medical practice. The book presents novel methods based on interval-valued fuzzy sets and the theory of their cardinalities.
This book discusses computer-supported medical diagnosis with a particular focus on ovarian tumor diagnosis – since ovarian cancer is difficult to diagnose and has high mortality rates, especially in Central and Eastern Europe. It presents the theoretical foundations (both medical and mathematical) of the intelligent OvaExpert system, which supports decision-making in tumor diagnosis. OvaExpert was created primarily to help gynecologists predict the malignancy of ovarian tumors by applying the existing diagnostic models and using modern methods of computational intelligence that accommodate imprecise and imperfect medical data, both of which are common features of everyday medical practice. The book presents novel methods based on interval-valued fuzzy sets and the theory of their cardinalities.
There are no comments for this item.