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Updated: Jun 9, 2025

An R-Based Landscape Validation of a Competing Risk Model
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提高心血管风险预测:开发一个先进的Xgboost模型,具有医院级随机效应

Tim Dong1, Iyabosola Busola Oronti2, Shubhra Sinha1

  • 1Bristol Heart Institute, Translational Health Sciences, University of Bristol, Bristol BS2 8HW, UK.

Bioengineering (Basel, Switzerland)
|October 25, 2024
PubMed
概括

一个新的二进制结果混合效应Xgboost (BME) 模型提高了对心血管结果的预测能力,特别是使用较小的数据集. 这种机器学习方法有效地处理相关的医院级数据,优于传统模型.

关键词:
在这里,我们可以看到AIAIAI.心血管医学心血管医学预期最大化最大化机器学习是机器学习.随机效应是一种随机效应.风险预测风险预测在Xgboost上使用.

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