A machine learning model using routine clinical data more accurately predicted 5-year heart failure risk in patients with CKD ...

Sepsis is one of the most common and lethal syndromes encountered in intensive care units (ICUs), and acute respiratory ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results ...

Machine learning model predicts poor pain-related quality of life after endometriosis surgery to assist clinicians with preoperative counseling.

Balancing nitrogen use is critical for maximizing crop yield while minimizing environmental and economic costs. A new approach integrates drone-based multispectral imaging with machine learning to ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

Abstract: Land surface temperature (LST) products require rigorous validation before widespread application, and the spatial representativeness of ground validation sites plays a critical role in ...

2 State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, China 3 Chongqing Key Laboratory of Hematology and Microenvironment, Chongqing, China Participants This study analysed 471 newly ...

The study, published in Forest Ecosystems, presents a refined update to the 3-PG (Physiological Processes Predicting Growth) model. Its major innovation is adding a carbon storage pool specifically ...