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Machine learning model boosts success of liver transplants from circulatory death donors
There are more candidates on the waitlist for a liver transplant than there are available organs, yet about half the time a ...
IOP Publishing’s Machine Learning series is the world’s first open-access journal series dedicated to the application and ...
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Machine-learning model could save costs, improve liver transplants, Stanford-led research shows
A machine learning-based model predicts how long it will take an organ donor to die after removing life support, aiding surgeons in deciding whether organs can be successfully transplanted.
Machine learning algorithms find patterns in human movement data collected by continuous monitoring, yielding insights that ...
Donation after circulatory death (DCD) procurements provide an opportunity to alleviate the limited organ supply for solid ...
The integration of AI, specifically machine learning, into physical security architecture shows promise in helping organizations better identify threats, improve response times and aid security ...
Haiqu's new encoding technique allows quantum computers to process high-dimensional financial data, showing improved ...
Prevailing AI architectures are not moving the needle. We need new ideas. Google Research proposes NL (nested learning). Here ...
AI’s Emerging Role in Healthcare Artificial intelligence is significantly reshaping healthcare as more advanced machine learning algorithms and foundation models become available. It’s impacting ...
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