整合人工智能与医疗行业链数据:通过语义知识图表增强临床营养研究
Deng Chen1, ChengJie Lu2, HongPeng Bai3
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China.
Frontiers in digital health
|October 18, 2024
概括
人工智能 (AI) 通过从复杂的医疗数据创建语义知识图表来增强临床营养研究. 这种方法改善了医疗行业链中的特征提取和决策.
科学领域:
- 临床营养研究 临床营养研究
- 医疗信息学 医疗信息学
- 人工智能应用程序 人工智能应用程序
背景情况:
- 医疗产业链产生多样化,多维的数据 (文本,图像,表格),这给处理和挖掘带来了挑战.
- 从异构的医学数据中提取有意义的特征是困难的,这阻碍了临床营养研究.
- 现有的方法难以应对医疗行业链数据的复杂性和规模.
研究的目的:
- 提出人工智能与医疗行业链数据的创新整合,用于知识图表构建和特征提取.
- 引入临床特征提取知识映射 (CFE-KM) 模型,用于增强知识图.
- 通过结构化语义知识,增强临床营养研究中的洞察力获取和决策.
主要方法:
- 开发了临床特征提取知识映射 (CFE-KM) 模型.
- 采用基于语法依赖规则的实体提取方法来增强知识图.
- 将CFE-KM模型应用于大规模的,现实世界的医疗行业链数据集.
主要成果:
- CFE-KM模型在关系提取,数据补充和特征提取方面表现强.
- 与竞争的基线方法相比,在处理医疗数据复杂性方面取得了优异的结果.
- 在结构化知识图中成功地表示了紧的语义知识,识别了知识差距.
结论:
- CFE-KM模型有效地解决了处理和挖掘异质医疗行业链数据的挑战.
- 人工智能驱动的知识图构建增强了特征提取,并为临床营养提供了宝贵的见解.
- 该模型通过结构化复杂的医疗数据和识别知识差距来改善决策过程.
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