论文标题
患者相似性：方法和应用
Patient similarity: methods and applications
论文作者
论文摘要
患者相似性分析在医疗保健应用中很重要。它将患者信息（例如他们的电子病历和遗传数据）作为输入，并计算患者之间的成对相似性。典型的患者相似性研究的程序可以分为几个步骤，包括数据整合，相似性测量和邻里识别。根据对患者相似性的分析，医生可以轻松找到最合适的治疗方法。有许多方法可以分析相似性，例如聚类分析。在机器学习期间，使用CNN等神经网络是一个新的热门话题。这篇评论总结了每个步骤中使用的代表性方法，并讨论了患者相似性网络的应用，尤其是在精确医学的背景下。
Patient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes the pairwise similarity between patients. Procedures of typical a patient similarity study can be divided into several steps including data integration, similarity measurement, and neighborhood identification. And according to an analysis of patient similarity, doctors can easily find the most suitable treatments. There are many methods to analyze the similarity such as cluster analysis. And during machine learning become more and more popular, Using neural networks such as CNN is a new hot topic. This review summarizes representative methods used in each step and discusses applications of patient similarity networks especially in the context of precision medicine.
