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Neural Networks for Prognostication of Patients With Heart Failure: Improving Performance Through the Incorporation of Breath-by-Breath Data From Cardiopulmonary Exercise Testing

机译:心力衰竭患者预后的神经网络:通过呼吸逐呼吸来自心肺运动测试的呼吸数据提高性能

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摘要

Background Prognostication of heart failure patients from cardiopulmonary exercise test (CPET) currently involves simplification of complex time-series data into summary indices. We hypothesized that prognostication could be improved by considering the totality of the data generated during a CPET, instead of using summary indices alone.
机译:从心肺运动试验(CPET)的心力衰竭患者的背景预测目前涉及将复杂的时间序列数据简化为摘要指标。 我们假设通过考虑在CPET期间生成的数据的整体来提高预后,而不是单独使用摘要指数。

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