用文本挖掘发现医学概念间隐藏关联
Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques
- 结合MetaMap与文本挖掘构建新索引结构
- 可发现跨文档的隐性医学关联
- 适合医疗知识图谱研究者使用
文本是当今计算机世界中最常见的数据存储方式。表面上看，这些数据似乎彼此无关，但实际上它们可能隐藏着深层联系。为此，本文提出一种新模型，利用MetaMap和适当的文本挖掘技术，发现两个医学概念之间的隐藏关系。该模型创建了一种新的综合索引结构，能够识别大多数现有方法忽略的跨文档隐性连接。实验表明，该模型在发现主题间新关联方面具有显著有效性。
原文摘要 · Abstract (English)
Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-mining techniques. Specifically, the model creates a new comprehensive index structure and can find cross-document hidden links connecting topics of interest that most existing approaches have ignored. Experiments show the effectiveness of the proposed model in discovering new connections between topics.
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