The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Yale Environment 360

Machine learning model predicts PTSD symptom severity over time

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 model predicts serious transplant complications months before symptoms appear

A powerful artificial intelligence (AI) tool could give clinicians a head start in identifying life-threatening complications ...
Forbes

How AI, Data Science, And Machine Learning Are Shaping The Future

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. We live in a world where machines can understand speech, ...

Machine learning algorithm fully reconstructs LHC particle collisions

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle ...
Medindia

Machine Learning Tool Predicts Post-Transplant Complications

BIOPREVENT’ AI tool predicts transplant-related immune conflict and mortality risk using biomarkers, helping doctors ...
Michigan Technological University

Researchers Apply Machine Learning and AI in Search of Less Costly Electronic Structure Method

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...
Kaleido Scope

Deep machine-learning speeds assessment of fruit fly heart aging and disease, a model for human disease

Researchers at the University of Alabama at Birmingham now show a way to significantly cut the time needed for that analysis while utilizing more of the heart region, using deep learning and ...

Machine learning helps solve a central problem of quantum chemistry

By applying new methods of machine learning to quantum chemistry research, Heidelberg University scientists have made significant strides in computational chemistry. They have achieved a major ...