All-cause mortality prediction in T2D patients with iTirps

作者：

Highlights：

• Temporal abstraction and frequent patterns for All-Cause Mortality Prediction

• A novel frequent patterns representation method

• An evaluation on real-life elderly T2D patients with CKD data

• Our methods outperformed sequential deep learning models based on raw data

摘要

•Temporal abstraction and frequent patterns for All-Cause Mortality Prediction•A novel frequent patterns representation method•An evaluation on real-life elderly T2D patients with CKD data•Our methods outperformed sequential deep learning models based on raw data

论文关键词：Pattern mining,Deep learning,Temporal data prediction

论文评审过程：Received 7 May 2021, Revised 17 May 2022, Accepted 17 May 2022, Available online 21 May 2022, Version of Record 3 June 2022.

论文官网地址：https://doi.org/10.1016/j.artmed.2022.102325