Integrating Kernel Methods into a Knowledge-Based Approach to Evidence-Based Medicine
Operational protocols are a valuable means for quality control. However, developing operational protocols is a highly complex and costly task. We present an integrated approach involving both intelligent data analysis and knowledge acquisition from experts that supports the development and validation of operational protocols. The aim is to lower development cost through the use of machine learning and at the same time ensure high quality standards for the protocol through empirical validation. We demonstrate our approach of integrating expert knowledge with data driven techniques based on our effort to develop an operational protocol for the hemodynamic system.