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Updated: Sep 18, 2025

An R-Based Landscape Validation of a Competing Risk Model
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基于机器学习的高级老年心力衰竭患者医院死亡风险模型的构建和验证

Shuai Shang1,2, Meng Wei1,2, Huasheng Lv1,2

  • 1Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People's Republic of China.

International journal of general medicine
|June 25, 2025
PubMed
概括

这项研究开发了一种机器学习模型,用于预测老年心力衰竭 (HF) 患者的住院死亡风险. 极端梯度增强 (XGB) 模型显示出高精度,有助于早期识别高风险个体.

关键词:
先进的高级老年人老年人死亡死亡死亡死亡死亡死亡心脏衰竭是因为心脏衰竭.机器学习是机器学习.

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Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
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Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
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