ICU Mortality Prediction Using XGBoost-based Scoring Systems: A Study from a Developing Country
Bibliographic record
Abstract
INTRODUCTION: Accurate mortality prediction in intensive care units (ICUs) is essential for enhancing patient outcomes and optimizing healthcare resource allocation. Traditional scoring systems, such as APACHE, APACHE II, and SAPS, have limitations in handling complex, high- -dimensional ICU data. In this study, multiple machine learning models were compared to establish an efficacious predictive model for mortality tailored explicitly to the Jordanian population and to explicate factors strongly associated with mortality. METHODS: This study was conducted as a single-center, retrospective cohort investigation, and the XGBoost machine learning algorithm was used to develop a novel ICU mortality prediction model. The model aimed to achieve superior prediction accuracy using a diverse set of readily available clinical data, including demographics, comorbidities, laboratory results, and medication groups. Model performance was evaluated against alternative machine learning algorithms, including logistic regression, conventionally employed in traditional scoring systems. RESULTS: Comparative analysis revealed that the XGBoost model performed better than other scoring systems, manifesting heightened accuracy (87.91%), sensitivity (92.88%), and Area Under the Receiver-Operating Characteristic Curve (AUC-ROC) Score/Curve (94.29%). Notably, the patient's length of hospital stays, albumin levels, and urea levels emerged as the most substantial predictors for ICU mortality, each exhibiting respective SHAP values of 0.5, 0.41, and 0.37. DISCUSSION: A locally adapted ICU mortality prediction model was developed, underscoring the pivotal role of predictors such as hospital stay duration, albumin, and urea levels in predicting patient outcomes. CONCLUSION: The heightened accuracy and sensitivity of the XGBoost model signify its potential as an invaluable tool in the critical task of mortality prediction within the Jordanian ICU context.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".