AI Medical Compendium Topic

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Disease Outbreaks

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Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt.

Frontiers in public health
OBJECTIVE: This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network wi...

Predicting malaria outbreak in The Gambia using machine learning techniques.

PloS one
Malaria is the most common cause of death among the parasitic diseases. Malaria continues to pose a growing threat to the public health and economic growth of nations in the tropical and subtropical parts of the world. This study aims to address this...

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

The Lancet. Planetary health
BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogene...

Predicting the transmission trends of COVID-19: an interpretable machine learning approach based on daily, death, and imported cases.

Mathematical biosciences and engineering : MBE
COVID-19 is caused by the SARS-CoV-2 virus, which has produced variants and increasing concerns about a potential resurgence since the pandemic outbreak in 2019. Predicting infectious disease outbreaks is crucial for effective prevention and control....

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach.

Computers in biology and medicine
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...

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Veterinary research
Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited dat...

AI-based epidemic and pandemic early warning systems: A systematic scoping review.

Health informatics journal
Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pan...

Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

The Lancet. Digital health
BACKGROUND: Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be pr...

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

Parasites & vectors
BACKGROUND: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factor...

Prediction of measles cases in US counties: A machine learning approach.

Vaccine
BACKGROUND: Although measles was declared eliminated from the United States in 2000, the frequency of measles outbreaks has increased in recent years. The ability to predict the locations of future cases could aid efforts to prevent and contain measl...