Using topological data analysis and pseudo time series to infer temporal phenotypes from electronic health records

作者：

Highlights：

• Topological Data and Pseudo Time Series to discover Type 2 Diabetes temporal phenotypes.

• Temporal phenotypes inferred from state-space model based on hidden-states transitions.

• Study of states continuous transitions visually delivered in an easily explainable way.

• Mined phenotypes characterized by significant differences in disease deterioration.

摘要

•Topological Data and Pseudo Time Series to discover Type 2 Diabetes temporal phenotypes.•Temporal phenotypes inferred from state-space model based on hidden-states transitions.•Study of states continuous transitions visually delivered in an easily explainable way.•Mined phenotypes characterized by significant differences in disease deterioration.

论文关键词：Type 2 diabetes,Unsupervised machine learning,Longitudinal studies,Electronic phenotyping

论文评审过程：Received 6 January 2020, Revised 21 May 2020, Accepted 11 July 2020, Available online 15 July 2020, Version of Record 24 July 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101930