【講座題目】 Semantic Association Mining

【講座嘉賓】 Prof .Ping Chen, University of Massachusetts Boston

【講座時(shí)間】7月21日（星期一）下午15：00-16：30

【講座地點(diǎn)】 bwin必贏唯一官網(wǎng)315教室

【摘要】  Discovery of risk factors affecting human health is very important. To medical researchers, these risk factors will provide valuable 

reasoning and modeling mechanism that are fundamentally important to medical and health research. In practice, health-related associations (risk factors) can provide basis for clinical decision making, health policy, and public guidance that directly impact 

health of individuals, families, communities, and populations. As the capability to capture and store medical data grows rapidly, 

the need for effective and efficient computation tools that facilitate such discoveries is high and increasing. This project aims to build an 

efficient medical association discovery system to extract significant, valid, non-redundant, and previously unknown associations 

of attributes (risk factors) from medical datasets. The goal and main innovations of this project are:

·         Integrating our knowledge-based approach with objective association mining method to generate only non-trivial, non-

redundant, valid, and previously unknown associations. These associations will serve as hypotheses and be further validated by biostatistic

 methods, which fundamentally changes current subjective formation of hypotheses to objective formation and discover radically different 

new knowledge; 

·         Building a research-grade medical association discovery system with a full suite of efficient and effective components: User Knowledge Acquisition 

Component, Semantic Network Building Component, Non-redundant Association Generation Component, Association Categorization 

Component, and Statistical Validation Component.