AIMC Topic: Severe Dengue

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Comparison of serum lactate and lactate-derived ratios as prognostic biomarkers in pediatric dengue shock syndrome using supervised machine learning models.

PloS one
BACKGROUND: Dengue shock syndrome (DSS), with critical complications encompassing mechanical ventilation (MV), dengue-associated acute liver failure (PALF), and encephalitis, is associated with high mortality in children. Although serum lactate is a ...

Circulating lncRNAs as biomarkers for severe dengue using a machine learning approach.

The Journal of infection
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
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
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
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
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
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
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...

Using Data Mining to Differentiate Dengue with Warning Signs from Severe Dengue: A Predictive Model from Oaxaca, Mexico.

The American journal of tropical medicine and hygiene
Dengue with warning signs (DWS) and severe dengue are significant public health concerns in tropical and subtropical regions globally. Accurate and timely differentiation between these clinical forms of dengue, although crucial, is often complex. In ...

Risk Stratification of Dengue Cases Requiring Hospitalization.

Journal of medical virology
Dengue pathogenesis involves immune-driven inflammation that contributes to severe disease progression. This study assessed a machine learning model to identify a minimal, yet highly predictive biomarker set, aiming to support clinical decision-makin...