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将生物医学本体学与生物转换器合并

Safaa Menad¹, Saïd Abdeddaïm¹, Lina F Soualmia¹

¹Univ Rouen Normandie, Normandie Univ, LITIS UR 4108 F-76000 Rouen, France.

Studies in health technology and informatics

|August 23, 2024

概括

本研究介绍了健康本体融合 (SiMHOMer) 的语模型,以整合生物医学知识. 该框架使用人工智能来发现新的关系,改善医疗数据组织和检索.

科学领域:

生物医学信息学生物医学信息学
医疗保健中的人工智能

背景情况:

实体学对于构建生物医学知识至关重要.
现有的本体学缺乏全面的概念和关系.
在医疗保健中,高效的数据结构,编码和检索是至关重要的.

研究的目的:

建议SIMHOMer (健康本体融合的姆模型) 用于医疗本体的语义融合.
最初将重点放在整合与疾病,症状,药物和不良事件有关的本体学上.
为增强的应用程序开发一种新的合并本体.

主要方法:

利用在生物医学数据上训练的姆神经模型 (BioSTransformers).
雇佣了生物转换器来识别新的概念关系并创建新的语义链接.
通过UMLS元词库和语义网络验证了方法并确定了关系.

主要成果:

证明了框架能够识别生物医学概念之间的新相关关系的能力.
成功地为本体学合并产生了新的语义关系.
使用UMLS元词库和语义网络进行初始验证显示出有希望的结果.

结论:

SiMHOMer框架为语义上合并医疗保健本体学提供了一个有希望的方法.
鉴定到的新关系有助于建立一个更全面,更综合的生物医学知识库.
这项研究为改善医疗数据管理和分析的应用铺平了道路.

关键词:

生物医学本体学是什么意思天体学融合融合西安人的神经模型这就是UMLS的UMLS.

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