面向智能诊疗的疾病文本知识表示体系

陈静,张文泰,陈清财,户保田,冯铭

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中文信息学报 ›› 2023, Vol. 37 ›› Issue (6) : 67-76.
知识表示与知识获取

面向智能诊疗的疾病文本知识表示体系

  • 陈静1,张文泰2,陈清财1,3,户保田1,冯铭2
作者信息 +

Knowledge Representation Framework of Disease Text for Intelligent Diagnosis and Treatment

  • CHEN Jing1, ZHANG Wentai2, CHEN Qingcai1,3, HU Baotian1, FENG Ming2
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摘要

近年来,深度学习技术在智能诊疗领域的应用已经取得了一定的进步,但是对专家知识的有效表示并为诊疗模型结果提供可解释性仍然是一个重要问题。大量的医疗文本资源包括医疗指南、专家共识、电子病历中蕴含着丰富的医学知识,以诊疗为目的,对这些文本中特定疾病的复杂医学知识进行准确的表示是一项极具意义的挑战。现有的医疗知识图谱无法从细粒度化、全面性方面满足对特定疾病的智能诊疗的应用需求。为此,该文以库欣综合征这一病种为实例,结合专家共识和真实电子病历数据,基于知识图谱和逻辑表达式的基本结构,构建了一套包含30余种实体、50余种关系以及因果逻辑表达式表示方法的面向诊疗的中文医疗文本知识表示体系,希望为疾病诊疗知识的表示和因果推理提供一些参考。

Abstract

A large number of medical text resources, such as medical guidelines, expert consensus and electronic medical records, contain rich knowledge. It is a challenge to construct and express the complex knowledge in these texts for supporting the medical diagnosis and treatment of specific diseases. Also, it is difficult to apply existing knowledge graphs directly for the above goal in terms of fine-grain and comprehensiveness. This paper constructs a Chinese medical knowledge representation framework for disease diagnosis and treatment by combining expert consensus, real-world electronic medical records of Cushing's syndrome, principles of knowledge graphs and logical expressions. It contains more than 30 types of entities, more than 50 types of relations and causal logical forms, providing a reference for knowledge representation and causal reasoning in disease diagnosis and treatment.

关键词

智能诊疗 / 知识图谱 / 因果知识 / 知识表示体系

Key words

intelligent diagnosis and treatment / knowledge graphs / causal knowledge / knowledge representation frameworks

引用本文

导出引用
陈静,张文泰,陈清财,户保田,冯铭. 面向智能诊疗的疾病文本知识表示体系. 中文信息学报. 2023, 37(6): 67-76
CHEN Jing, ZHANG Wentai, CHEN Qingcai, HU Baotian, FENG Ming. Knowledge Representation Framework of Disease Text for Intelligent Diagnosis and Treatment. Journal of Chinese Information Processing. 2023, 37(6): 67-76

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基金

国家自然科学基金(61872113,62006061);深圳市高等院校稳定支持计划面上项目(GXWD20201230155427003-20200824155011001);中国医学科学院医学与健康科技创新工程(CIFMS2021-I2M-1-003);北京市自然科学基金(M22013);广东省重点领域研发计划项目(2021B0101420005);中央高水平医院临床科研业务费(2022-PUMCH-C-012);国家重点研究与发展计划(2022ZD0116002)
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