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基于表征的依赖时间的共变量,改进了生存分析,以预测个体慢性病进展情况

Chen-Mao Liao1, Chuan-Tsung Su2, Hao-Che Huang1

  • 1Department of Applied Statistics and Information Science, Ming Chuan University, Taoyuan 333, Taiwan.

Biomedicines
|June 28, 2023
PubMed
概括

预测慢性病 (CKD) 的进展至关重要. 一个随机生存森林模型准确地确定了慢性病患者功能衰竭的危险因素,如肌素和年龄.

关键词:
卡普兰·梅耶尔 (Kaplan 梅耶尔) 是一个人工神经网络的人工神经网络慢性脏疾病 慢性脏疾病随机生存森林 随机生存森林

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