Septic shock prediction and knowledge discovery through temporal pattern mining
作者：
Highlights：
• A framework to identify relevant temporal patterns in data is proposed.
• Recent temporal pattern mining is combined with three feature selection techniques.
• Septic shock prediction models are trained using temporal patterns.
• There is a tradeoff between prediction performance and knowledge discovery.
• Contrasted grouping provides informative patterns of patient deterioration.
摘要
•A framework to identify relevant temporal patterns in data is proposed.•Recent temporal pattern mining is combined with three feature selection techniques.•Septic shock prediction models are trained using temporal patterns.•There is a tradeoff between prediction performance and knowledge discovery.•Contrasted grouping provides informative patterns of patient deterioration.
论文关键词：Temporal pattern mining,Sepsis,Electronic health records,Prediction,Pattern selection
论文评审过程：Received 6 January 2022, Revised 2 September 2022, Accepted 15 September 2022, Available online 21 September 2022, Version of Record 24 September 2022.
论文官网地址：https://doi.org/10.1016/j.artmed.2022.102406
