| 面向复杂决策和知识发现的医学知识不确定性计算方法 |
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| 引用本文： | 杜建. 面向复杂决策和知识发现的医学知识不确定性计算方法[J]. 医学信息学杂志, 2022, 43(7): 32-38 |
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| 作者姓名： | 杜建 |
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| 作者单位： | 北京大学健康医疗大数据国家研究院 北京 100191 |
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| 基金项目： | 国家自然科学基金面上项目“不确定性科学知识表示与计量的理论、方法与应用研究:以医学为例”(项目编号:72074006)；中国科协青年人才托举工程项目“医学知识结构化表示与智能化计算模型研究”(项目编号:2017QNRC001)。 |
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| 摘 要： | 将知识/证据的不确定性测度和结构化知识图谱相结合,总结并提出计算医学知识不确定性的几种方法,包括量表、概率、信息熵、证据-评论网络等。提出对于高确定性的知识,可由机器做决策；对于低确定性的知识,要触发人机交互,必须由机器和医生(科学家)共同决策,以此提高知识驱动的决策支持效率。
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| 关 键 词： | 不确定性 科学知识 概率 信息熵 证据-评论网络 |
Approaches on Measuring the Uncertainty of Medical Knowledge for Complex Decision Making and Knowledge Discovery |
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| DU Jian. Approaches on Measuring the Uncertainty of Medical Knowledge for Complex Decision Making and Knowledge Discovery[J]. Journal of Medical Informatics, 2022, 43(7): 32-38 |
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| Authors: | DU Jian |
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| Affiliation: | National Institute of Health Data Science, Peking University, Beijing 100191, China |
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| Abstract: | The paper combines the textual uncertainty knowledge (evidence) with structured knowledge graph, and puts forward several methods to calculate the uncertainty level of medical knowledge, including scale, probability, information entropy, evidence comment network and so on. It is proposed that the decision can be made by the machine for the knowledge with higher certainty level. For such conditions where there is only evidence with lower certainty level, it is important to join machines and doctors (scientists) together for shared decision-making, so as to improve the efficiency of knowledge-driven decision support. |
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