What is it about?
Sepsis is a common and life-threatening syndrome and a leading cause of morbidity and mortality globally. We performed a meta-analysis of observational studies to quantify the performance of a machine learning model to predict sepsis. For machine learning models, the pooled area under receiving operating curve (SAUROC) for predicting sepsis onset 3 to 4 h before, was 0.89 (95%CI: 0.86–0.92); sensitivity 0.81 (95%CI:0.80–0.81), and specificity 0.72 (95%CI:0.72–0.72).
Featured Image
Photo by Sharon McCutcheon on Unsplash
Why is it important?
Our study findings suggest that the machine learning approach had a better performance than the existing sepsis scoring systems in predicting sepsis.
Perspectives
The findings of our study suggest that machine learning prediction models could be implemented in the hospital in order to significantly reduce the in-hospital mortality rate, unnecessary hospital stay, and cost of treatment. An increased accuracy of sepsis identification could lead to better patient safety and, at the same time, save millions of dollars in large clinical settings. However, more studies are warranted to use the various multi-center databases, and more precise clinical variables need to be included to predict sepsis.
Md.Mohaimenul Islam
Taipei Medical University
Read the Original
This page is a summary of: Prediction of sepsis patients using machine learning approach: A meta-analysis, Computer Methods and Programs in Biomedicine, March 2019, Elsevier,
DOI: 10.1016/j.cmpb.2018.12.027.
You can read the full text:
Contributors
The following have contributed to this page
