AIMC Topic: Severe Dengue
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Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach.
The Journal of infection
Mar 14, 2025
OBJECTIVES: Dengue virus (DENV) infection is a significant global health concern, causing severe morbidity and mortality. While many cases present as a mild febrile illness, some progress to life-threatening severe dengue (SD). Early intervention is ...
Machine learning for predicting severe dengue in Puerto Rico.
Infectious diseases of poverty
Feb 4, 2025
BACKGROUND: Distinguishing between non-severe and severe dengue is crucial for timely intervention and reducing morbidity and mortality. World Health Organization (WHO)-recommended warning signs offer a practical approach for clinicians but have limi...
Synergistic modeling of hemorrhagic dengue fever: Passive immunity dynamics and time-delay neural network analysis.
Computational biology and chemistry
Jan 31, 2025
Dengue fever poses a formidable epidemiological challenge, particularly for vulnerable groups such as infants. This research paper establishes a mathematical model to describe the dynamics of secondary immunity in infants against dengue hemorrhagic f...
A machine learning-based risk score for prediction of mechanical ventilation in children with dengue shock syndrome: A retrospective cohort study.
PloS one
Dec 6, 2024
BACKGROUND: Patients 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 predi...
Severity prediction markers in dengue: a prospective cohort study using machine learning approach.
Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals
Nov 28, 2024
BACKGROUND: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.
Severe Dengue Prognosis Using Human Genome Data and Machine Learning.
IEEE transactions on bio-medical engineering
Feb 4, 2019
UNLABELLED: Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.
Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.
PloS one
May 11, 2015
BACKGROUND: In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when t...
Clinical correlations of plasma sphingosine-1-phosphate and sphingolipid key enzymes in severe dengue using laboratory and machine learning approach.
Clinica chimica acta; international journal of clinical chemistry
Jun 15, 2025
BACKGROUND: Sphingolipids are crucial for vascular integrity and cellular homeostasis, with recent studies highlighting their role in viral diseases.
Artificial Intelligence Approach for Severe Dengue Early Warning System.
Studies in health technology and informatics
Jan 25, 2024
Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases wi...
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