Syeda-Mahmood Tanveer
IBM Almaden Research Center, San Jose, CA 95120, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1135-41. doi: 10.1109/IEMBS.2010.5627132.
Mining medical reports can reveal important information correlating diagnosis with raw measurements helping in decision support. In this paper we address the problem of finding similar measurement reports for aiding clinical decision support. Specifically, we present a new approach to generating document class models for measurement reports using a multi-scale feature-value kernel. The class models serve as natural feature selection mechanism as well as indexes to large report collections. A document retrieval algorithm based on document class models is presented to enable similarity retrieval of pre-diagnosed reports. Collaborative filtering-guided assembly of associated disease labels is used to achieve clinical decision support.
挖掘医学报告可以揭示将诊断与原始测量相关联的重要信息,有助于决策支持。在本文中,我们解决了寻找相似测量报告以辅助临床决策支持的问题。具体而言,我们提出了一种使用多尺度特征值核来生成测量报告文档类模型的新方法。这些类模型既作为自然的特征选择机制,又作为大型报告集合的索引。提出了一种基于文档类模型的文档检索算法,以实现对预诊断报告的相似性检索。使用协同过滤引导的相关疾病标签组装来实现临床决策支持。