Light Tree-Based Models for Mortality Prediction of ICU Patients
Bibliographic record
Abstract
Numerous predictive models have been developed to assess in-hospital mortality risk of patients receiving intensive care, particularly those on mechanical ventilation. However, light tree-based models can accelerate clinical outcome predictions and enable timely interventions by physicians. An automated feature selection framework is proposed in this study, including Autofeat library, ReliefF and Fast CorrelationBased Filter (FCBF). The proposed framework was employed to eliminate most of the features before the prediction task. Treebased models - Bagging (BGG) and Extreme Gradient Boosting (XGB) - demonstrated comparable predictive accuracy using only 7 and 4 features for the imbalanced sample (S1.1) and balanced sample (S2.2), respectively, out of 67 original features. The BGG model, utilizing 7 features, achieved high predictive accuracy with an Area Under the Curve (AUC) of 0.89 (95 % confidence interval$[\text{CI}]: 0.87-0.90)$. In contrast, when applied to the S2.2 sample with only 4 features, both the XGB and BGG model yielded inferior results, as reflected by an AUC of 0.81 (95% CI: 0.79-0.83).
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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 | 0.003 | 0.005 |
| 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) | 0.002 | 0.001 |
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".