AIMC Topic: Disease Outbreaks
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Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.
PloS one
Sep 15, 2021
BACKGROUND: Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous.
Deep learning of contagion dynamics on complex networks.
Nature communications
Aug 5, 2021
Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiti...
EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and Internet-Sourced Data.
Sensors (Basel, Switzerland)
Jul 7, 2021
Due to the prevalence of globalization and the surge in people's traffic, diseases are spreading more rapidly than ever and the risks of sporadic contamination are becoming higher than before. Disease warnings continue to rely on censored data, but t...
Swarm Learning for decentralized and confidential clinical machine learning.
Nature
May 26, 2021
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an i...
A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.
Journal of medical Internet research
May 20, 2021
BACKGROUND: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the glob...
Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach.
PloS one
May 19, 2021
Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza a...
Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.
Frontiers in public health
May 5, 2021
Digital data sources have become ubiquitous in modern culture in the era of digital technology but often tend to be under-researched because of restricted access to data sources due to fragmentation, privacy issues, or industry ownership, and the me...
High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.
Foodborne pathogens and disease
Apr 26, 2021
The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or errone...
Inaccuracies in Google's Health-Based Knowledge Panels Perpetuate Widespread Misconceptions Involving Infectious Disease Transmission.
The American journal of tropical medicine and hygiene
Apr 26, 2021
Google health-based Knowledge Panels were designed to provide users with high-quality basic medical information on a specific condition. However, any errors contained within Knowledge Panels could result in the widespread distribution of inaccurate h...
A fusion of data science and feed-forward neural network-based modelling of COVID-19 outbreak forecasting in IRAQ.
Journal of biomedical informatics
Apr 22, 2021
BACKGROUND: Iraq is among the countries affected by the COVID-19 pandemic. As of 2 August 2020, 129,151 COVID-19 cases were confirmed, including 91,949 recovered cases and 4,867 deaths. After the announcement of lockdown in early April 2020, situatio...
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