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Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis.
Zhenyu Yang1, Xiaoju Cui2, Zhe Song3
1Kunming Medical University, Kunming, Yunnan, China.
BMC Infectious Diseases
|September 27, 2023
Summary
Machine learning effectively predicts sepsis onset. XGBoost and random forest models show the highest accuracy, aiding early detection and improving patient outcomes.
Area of Science:
- Computational biology
- Medical informatics
- Health services research
Background:
- Sepsis is a life-threatening condition with high mortality and economic burden.
- Early sepsis recognition is critical for effective treatment.
- Machine learning (ML) offers potential for predicting sepsis onset.
Conclusions:
- ML is effective for early sepsis prediction.
- XGBoost and random forest models demonstrate superior predictive performance.
- Further research into additional ML methods may enhance accuracy.
