ICU Admission Prediction for Patients With Kawasaki Disease or MIS-C Using Machine Learning
Bibliographic record
Abstract
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) and Kawasaki disease (KD) show a broad spectrum of clinical severity, from a relatively benign clinical course to requiring admission to the intensive care unit (ICU). With either, clinical deterioration may be rapid and unexpected. OBJECTIVES: The aim of the study was to develop a machine learning (ML) model to predict future ICU admission for patients with KD or MIS-C to augment clinical decision-making. METHODS: We developed a prediction model for ICU admission using 2,539 patients <18 years of age with MIS-C or KD enrolled in the International Kawasaki Disease Registry. Using discrete time-point clinical features and engineered time-series clinical features, we developed predictive snapshot and window ML models with logistic regression, XGBoost, and random forest. Performance was compared between the various iterations of the models. RESULTS: ML models effectively predicted admission to the ICU within the next 48 hours of the time of prediction. The time-series window-XGBoost model outperformed other models with an AUROC of 0.92 and an area under the precision-recall curve of 0.86. The incorporation of engineered time-series features improved the precision and recall independent of the length of the sampling time window. Higher ferritin level, treatment with anticoagulant or unfractionated heparin, higher C-reactive protein level, and lower platelet count were identified as the most predictive features for positive ICU prediction. CONCLUSIONS: ML algorithms can effectively predict ICU admission for pediatric patients with MIS-C or KD. These models may prompt physicians to pre-emptively implement supportive measures, possibly mitigating the risk of clinical deterioration.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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, 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".