Traditional financial distress prediction relies heavily on backward-looking financial indicators such as leverage, liquidity ...
Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting?
Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient ...
Learners who wish to receive a certificate must register for the exam scheduled on April 17, 2026, which will be conducted in two sessions - 9:30 am to 12:30 pm and 2 pm to 5 pm ...
ABSTRACT: Heart disease remains one of the leading causes of mortality worldwide, accounting for millions of deaths annually. Early detection of individuals at risk is essential for reducing ...
1 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA. 2 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA. 3 ...
Background Cancer survivors have an increased risk of heart failure, but this is balanced by the risk of death from other ...
This guest essay reflects the views of Nirali Somia, a graduate student at Cold Spring Harbor Laboratory. It is part of a series of essays from current researchers at the Cold Spring Harbor Laboratory ...
Abstract: Hypertension is a critical global health concern, necessitating accurate prediction models and effective prescription decisions to mitigate its risks. This study proposes a hybrid machine ...
Background: Enteral Nutrition-Associated Diarrhea (ENAD) is a common complication in critically ill patients, significantly impacting clinical outcomes. Accurately predicting the risk of ENAD is ...
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