Predicting Disease Outbreaks Using Social Media: Finding Trustworthy UsersBiosurveillanceSocial mediaTwitterTrustThe use of Internet data sources, in particular social media, for biosurveillance has gained atte
Fine lines and wrinkles are a natural part of aging that can be caused by hormonal changes, disease, smoking, sun damage, and aging.¹⁵ Integrating tretinoin into your skincare routine Elise Griffin, PA-C, shares her clinical experience:“When tretinoin is first started, irritation, dryne...
Bioassay data was retrieved from ChEMBL 25 using the associated Python module, which enables access to the API services via Python [27, 28]. The various inhibitory measures/endpoints, wherever possible, are standardized to nM units; the logarithm of the standardized values was used for machine ...
Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries...
Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of...
Prediction plots for the number of COVID-19 patients that would rise in the next 5 days for some countries, where an exponential increase in the curve is expected or the rise in the cases would remain constant. Various machine learning models were deployed for predicting the outbreak. The bla...
Hand-foot-mouth disease (HFMD) is a common infectious disease in children and is particularly severe in Guangxi, China. Meteorological conditions are known to play a pivotal role in the HFMD. Previous studies have reported numerous models to predict the
The Python package scikit-learn was used to con- vert the white space-joined texts into unigram tokens and calculate the token counts. The counts of tokens are pro- vided in Additional file 1. Search terms with counts larger than 1000 and belonging to ranges of infection sources, ...
the application of stacking in the field of infectious disease prediction is still relatively rare. While there have been a few studies that applied stacking to influenza prediction [16,23], more research is required to validate its effectiveness in predicting other infectious diseases such as infect...
The method presented a data-oriented approach that applies time-series analysis and association analysis to reveal meaningful hidden patterns for efficient handling of another pandemic crisis. ARIMA models have been successfully applied for predicting the disease outbreak. Several studies have utilized the...