Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.
Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.
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DOI：
10.1186/s13040-017-0123-y
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发表时间：
2017
期刊：
影响因子：
4.5
通讯作者：
Abidi SSR
中科院分区：
文献类型：
--
作者：
Mohammadhassanzadeh H;Van Woensel W;Abidi SR;Abidi SSR
获取完整的医学知识是一项挑战-通常是由于不完整的患者电子健康记录（EHR），但也因为隐藏在医生经验中的有价值的隐性医学知识。为了扩大覆盖面的不完整的医学知识为基础的系统超越其演绎封闭，从而提高他们的决策支持能力，我们认为，创新的，多策略的推理方法应该被应用。特别是，似是而非的推理机制应用模式从人类的思维过程，如泛化，相似性和插值，基于属性，层次和关系知识。似然推理机制包括归纳推理和类比推理，归纳推理概括了数据之间的共性以归纳出新的规则，类比推理由数据相似性指导以推断新的事实。通过进一步利用丰富的生物医学语义网本体来表示医学知识，包括已知的和试探性的，我们增加了合理推理的准确性和表达能力，并科普数据异构性，不一致性和互操作性等问题。在本文中，我们提出了一种基于语义Web的多策略推理方法，它集成了演绎推理和似是而非的推理，并利用语义Web技术来解决复杂的临床决策支持查询。我们使用肝炎患者的真实医疗数据集评估了我们的系统，从中随机删除了不同百分比的数据（5%，10%，15%和20%），以反映医疗知识不完整的情况。为了提高结果的可靠性，我们为每个百分比的缺失值生成了5个独立的数据集，这导致了20个实验数据集（除了原始数据集之外）。结果表明，合理推断的知识扩展了知识库的覆盖率，平均为2%，7%，12%和16%的数据集，分别为5%，10%，15%和20%的缺失值。这种知识库覆盖范围的扩展允许解决以前无法解决的复杂疾病诊断查询，而不会丢失答案的正确性。然而，与演绎推理相比，数据密集型似然推理机制产生显着的性能开销。我们观察到，合理的推理方法，通过产生试探性的推理和利用专家的领域知识，使我们能够扩展医学知识库的覆盖范围，从而改善临床决策支持。第二，通过利用OWL本体知识，我们能够提高似是而非的推理方法的表达能力和准确性。第三，我们的方法适用于一系列慢性疾病的临床决策支持系统。本文的在线版本（doi：10.1186/s13040-017-0123-y）包含补充材料，可供授权用户使用。
Capturing 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. We evaluated our system using a real-world medical dataset of patients with hepatitis, from which we randomly removed different percentages of data (5%, 10%, 15%, and 20%) to reflect scenarios with increasing amounts of incomplete medical knowledge. To increase the reliability of the results, we generated 5 independent datasets for each percentage of missing values, which resulted in 20 experimental datasets (in addition to the original dataset). The results show that plausibly inferred knowledge extends the coverage of the knowledge base by, on average, 2%, 7%, 12%, and 16% for datasets with, respectively, 5%, 10%, 15%, and 20% of missing values. This expansion in the KB coverage allowed solving complex disease diagnostic queries that were previously unresolvable, without losing the correctness of the answers. However, compared to deductive reasoning, data-intensive plausible reasoning mechanisms yield a significant performance overhead. We observed that plausible reasoning approaches, by generating tentative inferences and leveraging domain knowledge of experts, allow us to extend the coverage of medical knowledge bases, resulting in improved clinical decision support. Second, by leveraging OWL ontological knowledge, we are able to increase the expressivity and accuracy of plausible reasoning methods. Third, our approach is applicable to clinical decision support systems for a range of chronic diseases. The online version of this article (doi:10.1186/s13040-017-0123-y) contains supplementary material, which is available to authorized users.