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Pitt researchers and UPMC study AI to detect heart attacks faster

Pitt researchers and UPMC study AI to detect heart attacks faster

 


Researchers at the University of Pittsburgh and medical experts at UPMC are studying ways to speed up care for heart patients to save lives. They use artificial intelligence to make it happen. ” What this new research can accomplish is to identify new algorithms. So computerized interpretation of electrocardiograms is improved, and it really gives us insights to better identify patients who are having different types of heart attacks,” he said. says Christian Martin Gill of ECG peaks and troughs says: This story helps doctors determine if a patient is having the worst type of heart attack caused by a complete blockage of an artery. However, ECG patterns do not give the whole picture. “The question is, what if Mike had a heart attack?” A complete blockage of an artery? Well, maybe not. It may take 4 hours, 6 hours. So we keep doing blood tests over and over,” said Sarah Al-Zaiti, associate professor and research vice chair at the University of Pittsburgh. A detailed examination may take up to 24 hours, she said. Researchers said the new machine learning model is capable of diagnosis and diagnosis. Heart attacks can be classified more accurately and quickly. AI is said to help doctors look for hidden patterns and mine hundreds of features and data. “Look at everything you see on the ECG, every lead, every deflection, everything.” Measurements. In the end he completed nearly 600 features. This is the only item that can be measured with an electrocardiogram. Why not use AI?” Al-Zaiti said. “These new algorithms can identify heart attacks in a better way than existing monitors and clinicians working alone,” said Martin Gill. It’s about identifying how algorithms can best be used to help patients.

Researchers at the University of Pittsburgh and medical experts at UPMC are studying ways to treat heart patients faster to save lives.

They use artificial intelligence to make it happen.

“What this new study can accomplish is to identify a new algorithm that will allow the computer to better interpret the ECG, thus better identifying patients undergoing various types of heart attacks. It really gives us insight into what we can do,” said principal Christian Martin Gill. At UPMC he is in charge of EMS and Emergency Medicine.

The peaks and troughs of an EKG tell a story, helping doctors determine if a patient is having the worst type of heart attack caused by a complete blockage of an artery.

However, ECG patterns do not give the whole picture.

“The question is, what if Mike completely blocked the artery? It’s not showing up on the ECG, so I’m thinking a blood test would tell right away. Well, maybe not.” It can take four hours, six hours, so we keep doing blood tests,” said Sarah Al-Zaiti, associate professor and research vice chair at the University of Pittsburgh.

A thorough inspection could take up to 24 hours, he said. Researchers said new machine learning models could diagnose and classify heart attacks more accurately and quickly.

AI can help doctors look for hidden patterns and mine hundreds of features and data, they say.

“Look at everything you can see on an ECG, every lead, every deflection, every measurement. We ended up with almost 600 features. That’s how many things an ECG can measure. Why use Wouldn’t you?” Ai? said Al-Zaiti.

“These new algorithms can identify heart attacks in a better way than existing monitors and clinicians working alone,” said Martin Gill.

The next stage of research is to identify how the algorithm can best be used to help patients.

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