Survival analysis for sepsis patients: A machine learning approach to feature selection and predictive modeling
Bibliographic record
Abstract
Sepsis is a life-threatening condition that presents substantial challenges to healthcare and pharmacological management due to its high mortality rates and complex patient responses. Accurately predicting patient outcomes is essential for optimizing therapeutic interventions and improving clinical decision-making. This study evaluates the predictive performance of the Cox proportional hazards model and advanced machine learning techniques, such as extreme gradient boosting (XGBoost), gradient boosting machine (GBM), and random survival forests (RSF), in forecasting survival outcomes for sepsis patients. Feature selection methods, including adaptive elastic net (AEN), smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), and information gain (IG), were employed to refine model performance by identifying the most relevant clinical features. The results demonstrate that XGBoost consistently outperforms the Cox model, achieving a higher concordance index and demonstrating superior accuracy in handling complex, non-linear clinical interactions. The integration of feature selection further enhanced the machine learning models' predictive capabilities. These findings emphasize the potential of machine learning techniques to improve outcome prediction and guide personalized treatment strategies, offering valuable tools for critical care settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".