AIMC Topic: Dengue
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Metabolomic profiling of dengue infection: unraveling molecular signatures by LC-MS/MS and machine learning models.
Metabolomics : Official journal of the Metabolomic Society
Sep 21, 2024
BACKGROUND & OBJECTIVE: The progression of dengue fever to severe dengue (SD) is a major public health concern that impairs the capacity of the medical system to predict and treat dengue patients. Hence, the present study used a metabolomic approach ...
Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.
Scientific reports
Jul 26, 2024
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new c...
Methodology for the Differential Classification of Dengue and Chikungunya According to the PAHO 2022 Diagnostic Guide.
Viruses
Jul 6, 2024
Arboviruses such as dengue, Zika, and chikungunya present similar symptoms in the early stages, which complicates their differential and timely diagnosis. In 2022, the PAHO published a guide to address this challenge. This study proposes a methodolog...
Utilization of machine learning for dengue case screening.
BMC public health
Jun 11, 2024
Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in develo...
Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.
Computers in biology and medicine
Jun 4, 2024
This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorpora...
Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.
EBioMedicine
May 29, 2024
BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commerc...
Possible role of artificial intelligence in diagnosis of cases with non-specific signs and symptoms of dengue: A comment.
Clinics (Sao Paulo, Brazil)
May 15, 2024
Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.
Acta tropica
May 1, 2024
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collaps...
Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.
International journal of biometeorology
Mar 8, 2024
In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded...
Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.
International journal of biometeorology
Dec 27, 2023
In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted...
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