Patient similarity: methods and applications
This review paper is for researchers and practitioners interested in patient similarity analysis in healthcare, providing an overview of existing methods and applications.
This paper reviews methods for patient similarity analysis, which involves integrating patient data, measuring similarity, and identifying patient neighborhoods. The analysis helps doctors find suitable treatments, particularly in precision medicine.
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.