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

Researchers developed and validated a new lung cancer prediction model, Sybil-Epi, by integrating clinical and epidemiologic data with a pre-existing model.

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 ...

Mount Sinai researchers have created an analytic tool using machine learning that can predict cardiovascular disease risk in patients with obstructive sleep apnea ...

A new study published in Genome Research presents an interpretable artificial intelligence framework that improves both the accuracy and transparency of genomic prediction, a key challenge in fields ...

The severity of symptoms in posttraumatic stress disorder (PTSD) varies greatly across individuals in the first year after trauma and it remains difficult to predict whether someone might worsen, ...

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 ...

Infinite dilution activity coefficient is a key thermodynamic parameter in solvent design for chemical processes. Although ...

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 ...

Solar flares strong enough to knock out satellites and buckle power grids are, by definition, rare. That rarity is exactly ...