title: Ontology-based knowledge management for personalized adverse drug events detection.
abstract: Since Adverse Drug Event (ADE) has become a leading cause of death around the world, there arises high demand for helping clinicians or patients to identify possible hazards from drug effects. Motivated by this, we present a personalized ADE detection system, with the focus on applying ontology-based knowledge management techniques to enhance ADE detection services. The development of electronic health records makes it possible to automate the personalized ADE detection, i.e., to take patient clinical conditions into account during ADE detection. Specifically, we define the ADE ontology to uniformly manage the ADE knowledge from multiple sources. We take advantage of the rich semantics from the terminology SNOMED-CT and apply it to ADE detection via the semantic query and reasoning.
authors: IBM Research - China. caofeng@cn.ibm.com
authors: Feng
authors: F
authors: Cao
authors: Xingzhi
authors: X
authors: Sun
authors: Xiaoyuan
authors: X
authors: Wang
authors: Bo
authors: B
authors: Li
authors: Jing
authors: J
authors: Li
authors: Yue
authors: Y
authors: Pan
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001331:Automation; D003199:Computer Systems; D020000:Decision Support Systems, Clinical; D006801:Humans; D016347:Medical Records Systems, Computerized; D015203:Reproducibility of Results; D012308:Risk Management / Q000379:methods; D012660:Semantics; D012984:Software