ArticlePloS one2024
A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Tree-Based Machine Learning for Diagnostic Classification of Dengue Fever Using Routine Hematological Parameters: A Secondary Analysis of a Publicly Available Dataset.Diagnostics (Basel, Switzerland) · 2026Article
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- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Explainable extratreeclassifier model for early detection of type 2 diabetes: evidence from the PERSIAN Dena Cohort.BMC medical informatics and decision making · 2025Article
- Machine learning-based prediction of atherosclerotic cardiovascular disease risk in adults with diabetes or prediabetes.BMC cardiovascular disorders · 2025Article
- Dietary antioxidants and CKM-depression comorbidity: a primary analysis with a secondary evaluation of all-cause mortality using six machine learning algorithms.European journal of medical research · 2025Observational
- Machine learning-based prediction model for post-stroke cerebral-cardiac syndrome: a risk stratification study.Scientific reports · 2025Article
- Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.Lipids in health and disease · 2025Article
- In-Hospital Mortality in Mechanically Ventilated Children With Severe Dengue Fever: Explanatory Factors in a Single-Center Retrospective Cohort From Vietnam, 2013-2022.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population-Based Study.JMIR cardio · 2025Article
- Prediction of depressive disorder using machine learning approaches: findings from the NHANES.BMC medical informatics and decision making · 2025Article
- Machine learning for predicting severe dengue in Puerto Rico.Infectious diseases of poverty · 2025Article
- Article
- Associations of resuscitation fluid load, colloid-to-crystalloid infusion ratio and clinical outcomes in children with dengue shock syndrome.PLoS neglected tropical diseases · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with severe dengue who develop severe respiratory failure requiring mechanical ventilation (MV) support have significantly increased mortality rates. This study aimed to develop a robust machine learning-based risk score to predict the need for MV in children with dengue shock syndrome (DSS) who developed acute respiratory failure.
methodsThis single-institution retrospective study was conducted at a tertiary pediatric hospital in Vietnam between 2013 and 2022. The primary outcome was severe respiratory failure requiring MV in the children with DSS. Key covariables were predetermined by the LASSO method, literature review, and clinical expertise, including age (< 5 years), female patients, early onset day of DSS (≤ day 4), large cumulative fluid infusion, higher colloid-to-crystalloid fluid infusion ratio, severe bleeding, severe transaminitis, low platelet counts (< 20 x 109/L), elevated hematocrit, and high vasoactive-inotropic score. These covariables were analyzed using supervised models, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost). Shapley Additive Explanations (SHAP) analysis was used to assess feature contribution.
resultsA total of 1,278 patients were included, with a median patient age of 8.1 years (IQR: 5.4-10.7). Among them, 170 patients (13.3%) with DSS required mechanical ventilation. A significantly higher fatality rate was observed in the MV group than that in the non-MV group (22.4% vs. 0.1%). The RF and SVM models showed the highest model discrimination. The SHAP model explained the significant predictors. Internal validation of the predictive model showed high consistency between the predicted and observed data, with a good slope calibration in training (test) sets 1.0 (0.934), and a low Brier score of 0.04. Complete-case analysis was used to construct the risk score.
conclusionsWe developed a robust machine learning-based risk score to estimate the need for MV in hospitalized children with DSS.
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