The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-
being of patients. Human error hinders accurate diagnostics, as interpreting medical …

The integration of artificial intelligence technologies has propelled the progress of clinical
and genomic medicine in recent years. The significant increase in computing power has …

Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients.
Early prediction of sepsis is essential, as prompt and appropriate treatment can improve …

Background Breast cancer brain metastases (BCBM) are the most fatal, with limited survival
in all breast cancer distant metastases. These patients are deemed to be incurable. Thus …

Background Predicting hospital mortality risk is essential for the care of heart failure patients,
especially for those in intensive care units. Methods Using a novel machine learning …

Background: Sepsis, a life-threatening infection-induced inflammatory condition, has
significant global health impacts. Timely detection is crucial for improving patient outcomes …

Background: Mechanically ventilated patients in the intensive care unit (ICU) have high
mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology …

Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically
relevant events through machine learning models has gained particular attention. In the …

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous
development of medical technology in recent years, its morbidity and mortality are still high …