使用日常患者信息预测急症监护停留时间
Despoina Petsani1, Ioannis Makris1, Panagiotis Bamidis1
1Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Greece.
Studies in health technology and informatics
|August 8, 2025
概括
这项研究引入了一种新的句子转换模型,用于预测急性护理环境中的患者停留时间 (LoS),通过有效处理不规则的时间数据来更好地管理医疗保健资源,优于传统的LSTM模型.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 机器学习在医学中的应用
- 临床数据分析 临床数据分析
背景情况:
- 准确预测患者的住院时间 (LoS) 对医疗保健资源分配和临床决策至关重要.
- 现有的研究往往侧重于重症监护室 (ICU) 患者,在急症监护机构中留下了一个空白,复杂的,不规则的时间数据.
- 整合多式联运数据,包括时间临床信息和人口统计数据,是一个重大挑战.
研究的目的:
- 开发和评估一种新的机器学习模型,用于预测急性护理环境中的LoS.
- 为应对不规则和间歇性患者数据所带来的挑战.
- 为了比较基于句子变压器的方法与长短期记忆 (LSTM) 网络的有效性.
主要方法:
- 使用历史患者数据将LoS预测作为回归任务.
- 采用句子转换器,从多式联络数据中创建患者状况的详细嵌入.
- 利用历史趋势来管理不规则和数据缺口.
- 将新型句子转换器模型与基于LSTM的模型进行比较.
主要成果:
- 与LSTM模型相比,句子转换器模型表现出更高的性能.
- 拟议的模型有效地捕获了患者数据中的复杂模式.
- 初步结果表明,在LOS预测方面,准确性提高了.
结论:
- 句子转换器为急性护理中的LoS预测提供了一个有希望的方法,其性能优于LSTM.
- 该模型处理复杂时间数据的能力提高了临床决策和资源优化.
- 这项工作为更准确和临床相关的患者流量管理铺平了道路.
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