ArticleBMC pediatrics2018
Predicting the severity of dengue fever in children on admission based on clinical features and laboratory indicators: application of classification tree analysis.
Article in BMC pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 5 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Prognostic Models in Patients with Dengue: A Systematic Review.The American journal of tropical medicine and hygiene · 2025Pooled it
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Machine learning and deep learning techniques to support clinical diagnosis of arboviral diseases: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.PLoS neglected tropical diseases · 2022Pooled it
- Clinical signs and symptoms associated with WHO severe dengue classification: a systematic review and meta-analysis.Emerging microbes & infections · 2021Pooled it
- Article
- Atypical manifestations of dengue in pediatric patients hospitalized during the 2023-2024 epidemic in Argentina: a multicenter registry.European journal of pediatrics · 2026Article
- Early individualized risk prediction using clinical data for children during the febrile phase of dengue in outpatient settings in Vietnam and Thailand.PLOS digital health · 2026Article
- AI-Assisted Differentiation of Dengue and Chikungunya Using Big, Imbalanced Epidemiological Data.Tropical medicine and infectious disease · 2026Article
- Article
- Study on changes in a subset of full blood count parameters of patients with dengue fever and dengue haemorrhagic fever presented to teaching hospital - Kurunegala, Sri Lanka - a descriptive cross-sectional study.BMC infectious diseases · 2025Article
- Comparison of Predictive Models for Severe Dengue: Logistic Regression, Classification Tree, and the Structural Equation Model.The Journal of infectious diseases · 2025Article
- Prognostic prediction of dengue hemorrhagic fever in pediatric patients with suspected dengue infection: A multi-site study.PloS one · 2025Article
- Early Dengue Prediction in Bangladesh: A Comparative Study With Feature Analysis, Explainable Artificial Intelligence, and Model Optimization.Journal of tropical medicine · 2025Article
- A data science-based analysis of socioeconomic determinants impacting pediatric diagnostic radiology utilization during the COVID-19 pandemic.Pediatric radiology · 2024Article
- Early-phase factors associated with pediatric severe dengue in the Thai-Myanmar cross-border region.BMC public health · 2024Article
- Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.Viruses · 2024Article
- Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia.Insects · 2024Article
- Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach.Antibiotics (Basel, Switzerland) · 2024Article
- Possible role of artificial intelligence in diagnosis of cases with non-specific signs and symptoms of dengue: A comment.Clinics (Sao Paulo, Brazil) · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundDengue fever is a re-emerging viral disease commonly occurring in tropical and subtropical areas. The clinical features and abnormal laboratory test results of dengue infection are similar to those of other febrile illnesses; hence, its accurate and timely diagnosis for providing appropriate treatment is difficult. Delayed diagnosis may be associated with inappropriate treatment and higher risk of death. Early and correct diagnosis can help improve case management and optimise the use of resources such as hospital staff, beds, and intensive care equipment. The goal of this study was to develop a predictive model to characterise dengue severity based on early clinical and laboratory indicators using data mining and statistical tools.
methodsWe retrieved data from a study of febrile illness in children at Angkor Hospital for Children, Cambodia. Of 1225 febrile episodes recorded, 198 patients were confirmed to have dengue. A classification and regression tree (CART) was used to construct a predictive decision tree for severe dengue, while logistic regression analysis was used to independently quantify the significance of each parameter in the decision tree.
resultsA decision tree algorithm using haematocrit, Glasgow Coma Score, urine protein, creatinine, and platelet count predicted severe dengue with a sensitivity, specificity, and accuracy of 60.5%, 65% and 64.1%, respectively.
conclusionsThe decision tree we describe, using five simple clinical and laboratory indicators, can be used to predict severe cases of dengue among paediatric patients on admission. This algorithm is potentially useful for guiding a patient-monitoring plan and outpatient management of fever in resource-poor settings.
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