Latest AI and machine learning research in surveillance for healthcare professionals.
Twitter, as a social media platform, has become an increasingly useful data source for health survei...
An artificial neural network (ANN) model was developed to predict the risks of congenital heart dise...
Many men with low-risk prostate cancer (PCa) receive definitive treatment despite recommendations th...
Patient falls are a common safety event type that impairs the healthcare quality. Strategies includi...
In health question answering (QA) system development, question topic identification is crucial to un...
BACKGROUND: The diagnosis - and hence definitions - of healthcare-associated infections (HAIs) rely ...
The digital revolution has contributed to very large data sets (ie, big data) relevant for public he...
This study documents reporting errors in a sample of over 250,000 p-values reported in eight major p...
OBJECTIVES: In the field of laboratory medicine, minimizing errors and establishing standardization ...
The migration of imaging reports to electronic medical record systems holds great potential in terms...
Pathogen distribution models that predict spatial variation in disease occurrence require data from ...
Longitudinal studies play a key role in various fields, including epidemiology, clinical research, a...
Networks are well suited to display and analyze complex systems that consist of numerous and interli...
Reactivation of the hepatitis B virus (HBV) has been reported in patients receiving immunosuppressiv...
Data quality was placed as a major reason for the low utility of patient safety event reporting syst...
To compare term occurrences in free-text radiology reports and RSNA reporting templates, we selected...
Automated detection methods can address delays and incompleteness in cancer case reporting. Existing...
Over the past decade, improving healthcare quality and safety through patient safety event reporting...