Evolving classification of intensive care patients from event data
作者：
Highlights：
• We introduce a new paradigm for evolving classification of event data streams, such as patient data in Intensive Care Units.
• We present several alternative data mining approaches to evolving classification of event data streams.
• The alternative approaches are evaluated on a dataset of 3,452 episodes of adult patients (≥16 years of age).
• An incremental algorithm has produced the simplest and the most accurate models on Days 0 and 1.
• The regenerative approaches have reached better performance in terms of predictive accuracy starting with Day 2.
摘要
•We introduce a new paradigm for evolving classification of event data streams, such as patient data in Intensive Care Units.•We present several alternative data mining approaches to evolving classification of event data streams.•The alternative approaches are evaluated on a dataset of 3,452 episodes of adult patients (≥16 years of age).•An incremental algorithm has produced the simplest and the most accurate models on Days 0 and 1.•The regenerative approaches have reached better performance in terms of predictive accuracy starting with Day 2.
论文关键词：Evolving classification,Decision trees,Logistic regression,Event data streams,Intensive care
论文评审过程：Received 18 November 2015, Revised 16 April 2016, Accepted 19 April 2016, Available online 6 May 2016, Version of Record 10 May 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.04.001
