By comparing clinical cohorts and populations from Singapore and the US, researchers will study infectious diseases, corneal disorders, liver transplant outcomes, diabetes and lung cancer to uncover ...

Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

Competing machine-learning algorithms To predict the time of death, the model uses an array of clinical information from the donor including gender, age, body mass index, blood pressure, heart rate, ...

There are more candidates on the waitlist for a liver transplant than there are available organs, yet about half the time a match is found with a donor who dies after cardiac arrest following the ...

Smart health refers to the integration of cutting-edge technologies into healthcare systems to improve patient care and apply intelligent clinical decision-making. The study investigates how ...

Background: Liver failure is associated with high short-term mortality, and the predictive value of clinical factors for patients undergoing artificial liver therapy is uncertain. We aim to develop ...

Abstract: Fatty Liver Disease (FLD) is a medical condition that poses potential health risks and can affect individuals of various age groups, irrespective of demographic factors. The field of machine ...

1 Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL, USA. 2 Department of Mathematics and Computer Science, Islamic Azad University, Science and Research Branch, Tehran, ...

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a predictor of post-LT ARDS using machine learning ...

Abstract: Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, ...