机器学习风险估计和预测持续护理设施的死亡,使用行政数据
Faezehsadat Shahidi1, Elissa Rennert-May2,3,4,5,6,7, Adam G D'Souza8,9
1Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada.
Scientific reports
|October 18, 2023
概括
确定了在COVID-19期间面临死亡风险增加的持续护理居民. 诸如年龄,男性性别和并发症等因素显著影响了结果,突出了先进风险预测模型的需要.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 老年学是一门学科.
背景情况:
- 冠状病毒疾病2019 (COVID-19) 流行病对脆弱人群构成重大风险,包括持续护理居民.
- 在这个人口群体中确定死亡率预测因素对于有针对性的干预和资源分配至关重要.
研究的目的:
- 在COVID-19大流行期间确定与阿尔伯塔省持续护理居民死亡率相关的因素.
- 评估机器学习模型和预处理方法的性能,以预测死亡风险.
主要方法:
- 阿尔伯塔省持续护理居民的回顾性队列研究 (2020年3月 - 2021年3月).
- 链接的行政数据集用于全面的数据分析.
- 利用单变量和多变量逻辑回归 (LR) 来确定60天全因死亡率的预测因素.
- 采用尤登指数来确定最佳的灵敏度-特异性切线.
- 开发和比较各种用于绩效评估的机器学习模型.
主要成果:
- 增加的年龄,男性性别,出现症状,以前的入院和特定的并发症与更高的死亡率有显著的关联.
- 机器学习模型显示了风险预测的潜力,尽管需要进一步验证.
结论:
- 几种人口,临床和并发症因素预测了COVID-19大流行期间持续护理居民的死亡率.
- 机器学习和先进的预处理技术显示出增强传统方法之外的死亡风险预测的前景,需要进一步调查.
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