预测在重症监护室的意外再入院:一个多模式评估
Eitam Sheetrit1, Menachem Brief2, Oren Elisha3
1Microsoft R&D Center, Herzliya, Israel. eitams@microsoft.com.
Scientific reports
|September 18, 2023
概括
预测在重症监护室 (ICU) 计划外的再入院是非常重要的. 医生出院说明是预测再入院的最有效的数据来源,其表现优于其他数据类型.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床数据科学 临床数据科学
- 预测分析是一种预测分析.
背景情况:
- 住院再接收带来了重大挑战,增加了成本和风险.
- 由于病人的严重程度,重症监护室 (ICU) 面临着特殊的挑战.
- 准确预测计划外再入院的情况对于改善患者的治疗结果和医疗保健效率至关重要.
研究的目的:
- 评估不同数据模式的预测价值,以评估在ICU中的计划外再接收情况.
- 建立数据模式有效性的层次结构,以预测再接收.
- 为了研究的一致性,明确定义"计划外再接收".
主要方法:
- 利用最先进的机器学习进行时间序列分析和自然语言处理.
- 评估静态数据,自由文本,诊断/程序序列和时间序列数据的单独和综合有效性.
- 研究了时间抽象对时间序列预测性能的影响.
主要成果:
- 与所有其他方式相比,医生出院说明显示出对再入院的优越预测能力.
- 建立了预测回收预测中的各种数据类型的预测值层次.
- 时间抽象显著增强了基于时间序列的预测模型.
结论:
- 排放说明是预测ICU再接收的最有价值的数据来源.
- 对于可重复的研究来说,对非计划性再接收的明确定义至关重要.
- 整合不同的数据模式和先进的技术,如时间抽象可以提高预测准确性.
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