GRU-D-Weibull:一个新的实时个性化终点预测
Xiaoyang Ruan1, Liwei Wang1, Charat Thongprayoon2
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States; Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
Artificial intelligence in medicine
|December 2, 2023
概括
这项研究介绍了GRU-D-Weibull,这是一种使用电子健康记录 (EHR) 数据进行慢性疾病管理的新型预测模型. GRU-D-Weibull有效地处理缺失的EHR数据,用于实时风险预测和监测.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 数据为慢性疾病管理提供了潜力,但受到质量问题影响,阻碍了传统的预测建模.
- 预测模型在现实世界中的实施受限于电子健康记录中的数据质量挑战.
研究的目的:
- 提出和评估一种新的预测建模方法,GRU-D-Weibull,用于使用EHR数据实时个性化终点预测和风险管理.
- 评估GRU-D-Weibull在4期慢性病 (CKD4) 患者的性能和现实世界的可实施性.
主要方法:
- 开发了GRU-D-Weibull模型,该模型利用有衰变的封闭循环单位 (GRU-D) 来模拟用于终点预测的韦布尔分布.
- 使用CKD4患者队列 (n=6879) 对其他生存模型 (AFT,XGB,AFT,RSF,Nnet-survival) 进行系统评估GRU-D-Weibull.
- 使用诸如C指数和L1损失等指标比较性能,并尝试在工艺和后工艺校准.
主要成果:
- 与其他模型相比,GRU-D-Weibull表现出强大的预测性能 (C指数~0.77在4.3年后续) 和卓越的准确性 (L1-loss~0.45年在4年后续).
- 该模型有效地处理了缺失的EHR数据,随着更多的数据变得可用的后索引日期,预测错误减少.
- 训练后的重新校准使预测的生存概率与各种预测范围内的观察结果保持一致.
结论:
- GRU-D-Weibull在处理缺少的EHR数据方面具有优势,并为各种预测视野提供可靠的概率和点估计.
- 该模型显示了在慢性疾病中个性化终点风险管理的潜力,利用实时临床数据.
- 对数据质量影响和临床工作流集成的进一步研究是有必要的.
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