A machine learning model based on electronic health record data can provide updated predictions of preeclampsia risk, ...

New paired studies from the University of Minnesota Twin Cities show that machine learning can improve the prediction of floods. The studies, published in Water Resources Research and the Proceedings ...

Reliable and scalable water level prediction is crucial in hydrology for effective water resources management, especially ...

Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient's lungs, legs, feet, and other parts of the body. The condition is chronic ...

A new study suggests that lenders may get their strongest overall read on credit default risk by combining several machine learning models rather than relying on a single algorithm. The researchers ...

Machine learning analysis reveals which metrics drive March Madness seeding and predictive analytics in committee decisions.

A multimodal artificial intelligence (AI) model can identify patients at risk of intimate partner violence (IPV) years before ...

Researchers from UC Berkeley, Yale, Stanford’s Global Policy Laboratory, and NBER developed a deep learning method to predict how environmental regulations will affect wetlands and streams before they ...

Managing complex medical conditions often requires the simultaneous use of multiple different drugs, referred to as polypharmacy. While necessary, this significantly increases the risk of drug-drug ...

A new way to solve data scarcity: Turning qualitative reports into quantitative data with an LLM.

Computational analysis has revealed that diatomic molecules with two metal atoms of similar electronegativities can have a much greater dipole moment than those conventionally thought of as polar.

Reliable and scalable water level prediction is crucial in hydrology for effective water resources management, especially when considering challenges ...