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New Artificial Intelligence can predict if you'll die soon

New Artificial Intelligence can predict if you'll die soon


After looking at standard ECG tests, Artificial Intelligence (AI) can help identify patients most likely to die of any medical cause within a year, claim researchers. To reach this conclusion, researchers from Geisinger Health System in Pennsylvania analyzed the results of 1.77 million ECGs and other records from almost 400,000 patients. The team used this data to compare machine learning-based models that either directly analyzed the raw ECG signals or relied on aggregated human-derived measures (standard ECG features typically recorded by a cardiologist) and commonly diagnosed disease patterns.

The neural network model that directly analyzed the ECG signals was found to be superior for predicting one-year risk of death. Surprisingly, the neural network was able to accurately predict risk of death even in patients deemed by a physician to have a normal ECG.
Three cardiologists separately reviewed the ECGs that had first been read as normal, and they were generally unable to recognize the risk patterns that the neural network detected, researchers said.

"This is the most important finding of this study. This could completely alter the way we interpret ECGs in the future," said Brandon Fornwalt, chair of the Department of Imaging Science and Innovation at Geisinger in Danville, Pennsylvania. Another study by the same group of researchers found that AI-based models can analyse ECG test results and pinpoint patients at higher risk of developing a potentially dangerous irregular heartbeat (arrhythmia).

The team used more than two million ECG results from more than three decades of archived medical records in Pennsylvania/New Jersey's Geisinger Health System to train deep neural networks. They found that Artificial intelligence can examine ECG test results, to predict irregular heartbeat and the death risk, according to the two preliminary studies to be presented at the American Heart Association's Scientific Sessions 2019 in Philadelphia from November 16-18. Read More...

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