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Demystifying patient similarity: A guide to computational methods for personalized medicine

Published on:

1 December 2020

Primary Category:

Machine Learning

Paper Authors:

Leyu Dai,

He Zhu,

Dianbo Liu

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Key Details

Patient similarity facilitates personalized predictive models, disease subtyping and treatment design

Main steps: data integration, similarity measurement, neighborhood identification

Methods include distance metrics, neural networks, clustering and active learning

Key applications are in precision medicine and clinical decision support

AI generated summary

Demystifying patient similarity: A guide to computational methods for personalized medicine

This paper reviews computational methods for analyzing patient similarity from electronic health records and other biomedical data. It summarizes key techniques for data integration, similarity measurement, neighborhood identification and applications in precision medicine. The review aims to provide an accessible introduction to this emerging field.

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