  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.
