基于医学文献发现概念间的隐藏关联
Finding New Connections between Concepts from Medline Database Incorporating Domain Knowledge
- 改进Swanson ABC模型，引入领域知识发现医学概念间隐性连接
- 通过共同相关主题B，揭示无直接关联的医学概念A与C之间的联系
- 适用于医学知识挖掘，帮助研究人员发现跨领域新假设
在数字化时代，数据无所不在并深刻影响日常生活。有趣的是，在这个小型世界中，一切皆属于一个生态系统，彼此相连，或直接或间接。数据亦如此。大多数情况下，某个主题看似与其他主题无关，但实际上可通过一个共同关联主题建立联系。因此，本研究提出一种改进的自适应模型，基于Don R. Swanson提出的文献发现（Literature-Based Discovery, LBD）经典ABC模型，用于发现医学概念间的隐藏关联。该模型表明，两个看似无关的概念A和C，可通过一个共同相关主题B实现连接。本文将此经典模型应用于医学概念的关联分析，以挖掘潜在的新科学假设。
原文摘要 · Abstract (English)
In this digital world, data is everything and significantly impacts our everyday lives. Interestingly, in this small world, everything is part of an ecosystem, where everything is connected, directly or indirectly. The same thing happens to data as well. In most cases, it may seem like a particular topic does not have any connection with another one, but in reality, they are connected through a mutually related topic. Therefore, in this research, we will discuss an adaptive model modified from the ABC model by Don R. Swanson, a Literature-Based Discovery (LBD) Model, to find the hidden connections between Concepts of Interest. The model demonstrates that two topics, A and C are different and have no relationship. But they have a common topic, B that can be used to connect topics A and C This famous model will be used in this discussion to connect Medical Concepts.
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