To develop an interpretable model to predict 28-day mortality risk in ICU patients with sepsis using clinical data from a Chinese cohort.
Key Findings:
Seven predictive indicators for sepsis mortality were identified through LASSO regression.
The median age of patients was 68 years, with 60.2% being male.
No significant differences were found in baseline characteristics between training and validation sets.
Interpretation:
The study demonstrates the potential of machine learning models in predicting mortality risk in sepsis patients, highlighting the importance of early identification for improving clinical outcomes.
Limitations:
The study was conducted in a single center, which may limit generalizability.
The retrospective nature of the study may introduce biases that could affect the results.
Conclusion:
Machine learning models can effectively predict mortality in ICU patients with sepsis, aiding in timely clinical interventions.
