Artificial Intelligence (AI) advances are pushing the boundaries across research domains
with AI-driven solutions in healthcare claiming a significant share. A key objective of these …

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis
significantly increases the risk of death, highlighting the importance of accurately predicting …

There are significant regional inequities in health resources around the world. It has become
one of the most focused topics to improve health services for data-scarce hospitals and …

Sepsis remains a complex, life-threatening condition characterized by an overwhelming
immune response to infection, leading to high mortality rates in hospital settings. Rapid and …

Sepsis is blood poisoning disease that occurs when body shows dysregulated host
response to an infection and cause organ failure or tissue damage which may increase the …

This study proposes the use of Machine Learning models to predict the early onset of sepsis
using deidentified clinical data from Montefiore Medical Center in Bronx, NY, USA. A …

Federated learning (FL) is a privacy preserving approach to learning that overcome issues
related to data access, privacy, and security, which represent key challenges in the …

We present a generalized linear structural causal model, coupled with a novel data-adaptive
linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By …

Sepsis is a major health issue causing mortality, morbidity and health care financial crisis to
people around the globe. To resolve this issue, many researchers and clinical practitioners …

The rapid advancement in sensing and information technology has ushered us into an era of
data explosion, where a large amount of data is now easily available and accessible in the …