AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Dengue

Showing 1 to 10 of 50 articles

Clear Filters

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.

Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks.

Scientific reports
Infectious diseases significantly impact both public health and economic stability, underscoring the critical need for precise outbreak predictions to effictively mitigate their impact. This study applies advanced machine learning techniques to forec...

Modeling and analysis of dengue transmission in fuzzy-fractional framework: a hybrid residual power series approach.

Scientific reports
The current manuscript presents a mathematical model of dengue fever transmission with an asymptomatic compartment to capture infection dynamics in the presence of uncertainty. The model is fuzzified using triangular fuzzy numbers (TFNs) approach. Th...

Predicting the infecting dengue serotype from antibody titre data using machine learning.

PLoS computational biology
The development of a safe and efficacious vaccine that provides immunity against all four dengue virus serotypes is a priority, and a significant challenge for vaccine development has been defining and measuring serotype-specific outcomes and correla...

Smart monitoring solution for dengue infection control: A digital twin-inspired approach.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In the realm of smart healthcare, precise monitoring and prediction services are crucial for mitigating the impact of infectious diseases. This study introduces an innovative digital twin technology-inspired monitoring archi...

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...

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 ...

Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques.

Parasites & vectors
BACKGROUND: Mosquito-borne diseases cause millions of deaths each year and are increasingly spreading from tropical and subtropical regions into temperate zones, posing significant public health risks. In the Basque Country region of Spain, changing ...

Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects.

BMC public health
BACKGROUND: Dengue fever is a mosquito-borne viral disease that poses significant health risks and socioeconomic challenges in Brazil, necessitating accurate forecasting across its 27 federal states. With the country's diverse climate and geographica...

Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference.

Scientific reports
Over the last fifty years, arboviral infections have made an unparalleled contribution to worldwide disability and morbidity. Globalization, population growth, and unplanned urbanization are the main causes. Dengue is regarded as the most significant...