Title :
Optimized model tuning in medical systems
Author :
Klema, Jiri ; Kubalik, Jiri ; Palous, Jiri
Author_Institution :
Dept. of Cybern., Czech Tech. Univ., Prague, Czech Republic
Abstract :
For patients considering elective major surgery, information about operative mortality risks is essential for careful decision making. To help patients and surgeons make informed decisions about whether to undergo elective high-risk surgery, a reliable predictive model would be beneficial. This paper focuses on development and optimized tuning of a model predicting risks related to heart interventions of several types. The model is based oil representative data sets collected in the Merged National Registry (MNR) on Cardiovascular Interventions. The registry is operated and governed by the MEDICON Center. The central attention is paid to an instance-based reasoning model and its tuning. In particular, the paper presents and discusses benefits of utilizing a genetic algorithm with limited convergence for this purpose.
Keywords :
cardiology; case-based reasoning; convergence; genetic algorithms; medical expert systems; modelling; prediction theory; safety; surgery; tuning; MEDICON Center; Merged National Registry on Cardiovascular Interventions; convergence; elective major surgery; genetic algorithm; heart interventions; high-risk surgery; informed decision making; instance-based reasoning model; medical systems; operative mortality risks; optimized model tuning; predictive model; Cardiology; Convergence; Genetic algorithms; Heart; Medical diagnostic imaging; Medical services; Predictive models; Statistics; Surgery; Valves;
Conference_Titel :
Computer-Based Medical Systems, 2002. (CBMS 2002). Proceedings of the 15th IEEE Symposium on
Print_ISBN :
0-7695-1614-9
DOI :
10.1109/CBMS.2002.1011355