A machine learning model bests traditional methods for predicting cirrhosis mortality among hospitalized patients.

Researchers employed a machine learning technique known as random forest analysis and found that it significantly outperformed traditional methods in predicting which hospitalized patients with ...

For inpatients with cirrhosis, a machine learning (ML) model using random forest (RF) analysis is superior for prediction of inpatient mortality, according to a study published online July 23 in ...

Findings from a new study published in Annals of Family Medicine show that a machine learning model accurately predicts ...

The Random Forest model achieved an AUC of 0.859 when using most of the covariates and a comparable AUC of 0.851 when using only the top 15 predictive variables.

This study examined whether machine learning could predict the risk and contributing factors of no-shows and late ...

In the global trend of vigorously developing hydrogen energy, proton-conducting solid oxide electrolysis cells (P-SOECs) have ...

Our news correspondents obtained a quote from the research from University of Minnesota: “To better predict the demand for health insurance, three regression models in machine learning - random ...