Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support
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- 作者：Hossein Mohammadhassanzadeh ; William Van Woensel ; Samina Raza Abidi…
- 关键词：Medical knowledge bases ; Semantic Web reasoning ; Plausible reasoning ; Inductive generalization ; Analogical reasoning
- 刊名：BioData Mining
- 出版年：2017
- 出版时间：December 2017
- 年：2017
- 卷：10
- 期：1
- 全文大小：3827KB
- 刊物主题：Computer Appl. in Life Sciences; Computational Biology/Bioinformatics; Data Mining and Knowledge Discovery; Bioinformatics; Algorithms;
- 出版者：BioMed Central
- ISSN：1756-0381
- 卷排序：10
文摘
BackgroundCapturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians’ experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, plausible reasoning mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge. Plausible reasoning mechanisms include inductive reasoning, which generalizes the commonalities among the data to induce new rules, and analogical reasoning, which is guided by data similarities to infer new facts. By further leveraging rich, biomedical Semantic Web ontologies to represent medical knowledge, both known and tentative, we increase the accuracy and expressivity of plausible reasoning, and cope with issues such as data heterogeneity, inconsistency and interoperability. In this paper, we present a Semantic Web-based, multi-strategy reasoning approach, which integrates deductive and plausible reasoning and exploits Semantic Web technology to solve complex clinical decision support queries.