This predictive model built on readily acquired clinical data provides encouraging results for the detection of residual disease. External validation and prospective studies implementing the model in ...

An artificial intelligence (AI) model developed by researchers at The University of Texas MD Anderson Cancer Center ...

Two complementary predictors (DAAE-M and ELIE) estimate individualized 5-year progression risk using routine clinical data, ...

To generate usable data, NSWCPD engineers built a controlled test environment and introduced faults such as air leaks, inlet ...

Machine learning models can predict the risk for developing moderate-to-severe persistent asthma and allergic rhinitis in ...

Final random-forest-based models outperformed all publicly available risk scores on internal and external test sets.

A machine learning model can accurately predict an individual’s risk of developing hepatocellular carcinoma (HCC) using routine clinical data, according to a new study. The findings point to a ...

A team of researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time -- a development that could lead to the creation of stronger, more ...

Machine learning algorithms utilizing electronic health records can effectively predict two-year dementia risk among American Indian/Alaska Native adults aged 65 years and older, according to a ...

In my latest Signal Spot, I had my Villanova students explore machine learning techniques to see if we could accurately ...