Researchers at General Hospital of Ningxia Medical University have introduced a machine learning model based on XGBoost to predict sepsis 24 hours post-admission in elderly patients. Using LASSO regression for feature selection, they identified critical markers such as baseline APTT and lymphocyte count, marking a significant step forward in early sepsis diagnostics.
Q&A
- What role does XGBoost play in this model?
- How is LASSO regression utilized in the study?
- How does the early warning model benefit clinical decision-making?