A scoping review of the use of Twitter for public health research
O Edo-Osagie, B De La Iglesia, I Lake… - Computers in biology and …, 2020 - Elsevier
Public health practitioners and researchers have used traditional medical databases to
study and understand public health for a long time. Recently, social media data, particularly …
study and understand public health for a long time. Recently, social media data, particularly …
Machine learning for data-centric epidemic forecasting
The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision
makers in multiple domains, ranging from public health to the economy. Forecasting …
makers in multiple domains, ranging from public health to the economy. Forecasting …
Combining search, social media, and traditional data sources to improve influenza surveillance
We present a machine learning-based methodology capable of providing real-time
(“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from …
(“nowcast”) and forecast estimates of influenza activity in the US by leveraging data from …
A review of influenza detection and prediction through social networking sites
A Alessa, M Faezipour - Theoretical Biology and Medical Modelling, 2018 - Springer
Early prediction of seasonal epidemics such as influenza may reduce their impact in daily
lives. Nowadays, the web can be used for surveillance of diseases. Search engines and …
lives. Nowadays, the web can be used for surveillance of diseases. Search engines and …
[HTML][HTML] The application of internet-based sources for public health surveillance (infoveillance): systematic review
Background Public health surveillance is based on the continuous and systematic collection,
analysis, and interpretation of data. This informs the development of early warning systems …
analysis, and interpretation of data. This informs the development of early warning systems …
Humanitarian health computing using artificial intelligence and social media: A narrative literature review
L Fernandez-Luque, M Imran - International journal of medical informatics, 2018 - Elsevier
Abstract Introduction According to the World Health Organization (WHO), over 130 million
people are in constant need of humanitarian assistance due to natural disasters, disease …
people are in constant need of humanitarian assistance due to natural disasters, disease …
Machine-learned epidemiology: real-time detection of foodborne illness at scale
Abstract Machine learning has become an increasingly powerful tool for solving complex
problems, and its application in public health has been underutilized. The objective of this …
problems, and its application in public health has been underutilized. The objective of this …
Exploring trends of nonmedical use of prescription drugs and polydrug abuse in the Twittersphere using unsupervised machine learning
Introduction Nonmedical use of prescription medications/drugs (NMUPD) is a serious public
health threat, particularly in relation to the prescription opioid analgesics abuse epidemic …
health threat, particularly in relation to the prescription opioid analgesics abuse epidemic …
Simnest: Social media nested epidemic simulation via online semi-supervised deep learning
Infectious disease epidemics such as influenza and Ebola pose a serious threat to global
public health. It is crucial to characterize the disease and the evolution of the ongoing …
public health. It is crucial to characterize the disease and the evolution of the ongoing …
Syndromic surveillance of Flu on Twitter using weakly supervised temporal topic models
Surveillance of epidemic outbreaks and spread from social media is an important tool for
governments and public health authorities. Machine learning techniques for nowcasting the …
governments and public health authorities. Machine learning techniques for nowcasting the …