Predictive modelling of survival and length of stay in critically ill patients using sequential organ failure scores
作者：
Highlights：
• A dataset of 14,480 critically ill patients within the ICU was collected.
• Machine learning models are constructed to predict patient length of stay and mortality.
• Prediction accuracy was improved by using a two-by-two classification grid.
• Probabilistic model outputs can improve interpretation by physicians.
• Moving data window shows potential for real-time ICU patient analysis.
摘要
Highlights•A dataset of 14,480 critically ill patients within the ICU was collected.•Machine learning models are constructed to predict patient length of stay and mortality.•Prediction accuracy was improved by using a two-by-two classification grid.•Probabilistic model outputs can improve interpretation by physicians.•Moving data window shows potential for real-time ICU patient analysis.
论文关键词：Mortality prediction,Length of stay modeling,Support vector machines,Critical care,Sequential organ failure score
论文评审过程：Received 12 September 2014, Revised 8 December 2014, Accepted 20 December 2014, Available online 30 December 2014.
论文官网地址：https://doi.org/10.1016/j.artmed.2014.12.009
