Deep Learning Predicts Heart Failure

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MIT News

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Health

Can deep learning transform heart failure prevention?

Summary

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88% Informative

Researchers from MIT and Harvard Medical School introduced a noninvasive deep learning approach that analyzes electrocardiogram ( ECG ) signals to accurately predict a patient’s risk of developing heart failure.

In a clinical trial, the model showed results with accuracy comparable to gold-standard but more-invasive procedures.

The goal of this work is to identify those who are starting to get sick even before they have symptoms.

CHAIS is a noninvasive approach to estimating left atrial pressure using a widely available cardiac monitor.

The researchers have another ongoing clinical trial with MGH and Boston Medical Center that they hope to conclude soon to begin data analysis.

“The real promise of AI in health care is to provide equitable, state-of-the-art care to everyone, regardless of their socioeconomic status, background, and where they live,” Stultz says.

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semi-formal

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English

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not offensive

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not hateful