Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging - npj Digital Medicine

Artificialintelligence may predict heart attacks NaturePortfolio

) – providing an explanation of the highest predicted risk that highlights how the polar maps and clinical features contribute to the overall risk. The waterfall plot allows for visualization of both the extent of influence and direction . In the presented case, a 41-year-old female with heart failure with moderately reduced ejection fraction and moderate perfusion deficits is identified as having a high risk of death.

Previous prognostic studies estimated risk jointly using composite adverse events and without the use of time-to-event data. A high-risk of death in the next year is a very different scenario than a risk of hospital admission or revascularization over 10 years – but our current presentation and assessment of data lack this granularity.

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