利用深度生存模型来预测各种医院再入院的护理质量风险
Nhat Quang Tran1, Gautam Goel1, Nirmala Pudota2
1AI Center of Excellence, Deloitte & Touche LLP, New York, USA.
Scientific reports
|June 28, 2023
概括
高的住院再接收率带来了财务负担. 机器学习生存分析有效地预测了再接收风险,SparseDeepWeiSurv模型显示了评估医疗保健质量的最佳表现.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 生存分析的分析.
背景情况:
- 在全球范围内,高的住院再接收率严重加重了医疗保健系统的负担.
- 再接收率是评估医疗保健提供者护理质量的关键指标.
- 预测再入院对于改善患者的治疗结果和降低医疗保健成本至关重要.
研究的目的:
- 评估基于机器学习的生存分析,以评估医院再入院治疗质量的风险.
- 探索先进的特征表示技术,以预测再接收风险.
- 确定最有效的生存模型来预测医院再入院.
主要方法:
- 将各种生存模型应用于医疗保健索赔数据集,包括患者人口统计和出院信息.
- 利用高级功能表示技术,如BioBERT和Node2Vec用于诊断代码.
- 采用基于深度学习的生存分析模型来预测再接收风险,独立于特定条件或固定再接收窗口.
主要成果:
- 该SparseDeepWeiSurv模型,使用韦布尔分布来计算回收时间,证明了优越的区分能力和校准.
- 对诊断代码的高级功能嵌入 (BioBERT,Node2Vec) 并没有提高模型性能.
- 模型性能因评估时间点而异,这表明需要适应性模型选择.
结论:
- 基于深度学习的生存分析是有效的估计医院再入院的护理质量风险.
- 选择生存模型可能需要依赖时间,以获得最佳的护理质量评估.
- 未来的研究应该考虑再接收风险预测的时间动态.
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