对于预测医院再入院的生存模型和纵向医学事件
Sacha Davis1, Russell Greiner2,3
1Computing Science, University of Alberta, 116 St & 85 Ave, Edmonton, AB, T6G 2R3, Canada. sdavis1@ualberta.ca.
BMC health services research
|November 13, 2024
概括
使用生存模型和患者病史序列,预测30天再入院的情况得到了改善. 这种方法比传统的医疗保健质量改善方法提供了更高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 预测分析是一种预测分析.
背景情况:
- 住院再接收率影响医疗保健的资金和医疗保健的质量.
- 准确的预测模型对于金融稳定和患者的治疗结果至关重要.
- 生存预测模型通过利用回收数据的时间来提供一种新的方法.
研究的目的:
- 为了证明一种以生存为灵感的模型在预测30天所有原因的医院再入院的有效性.
- 评估纵向患者表示和机器学习医学知识对预测准确性的影响.
- 将生存模型与传统方法比较,例如基于LACE的模型.
主要方法:
- 分析了421,088名患者的人口水平队列.
- 临床特征和历史医学代码序列被用作模型输入.
- 训练了二进制 (XGBoost,DNN) 和时间到事件 (CoxPH,N-MTLR) 模型,并使用AUROC@30,一致性和其他指标进行了比较.
主要成果:
- 使用序列特征的模型显著优于仅使用临床特征的模型.
- 与二进制分类相比,时间到事件的目标改善了预测性能.
- 在N-MTLR模型中,平均AUROC@30达到0.8460,超过LACE基线 (0.6587).
结论:
- 行政医疗代码的序列为再接收提供了有价值的预测信息.
- 将先前的医学知识整合到模型中可以提高培训和绩效.
- 利用时间到事件数据的生存模型显示了利用行政数据进行再接收预测的绝佳潜力.
相关概念视频
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