住院时间预测计划入院使用观察医疗结果 合作伙伴关系 共同数据模型:回顾性研究
Haeun Lee1,2, Seok Kim2, Hui-Woun Moon2
1Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, United States.
Journal of medical Internet research
|November 22, 2024
概括
使用标准化的观察医疗结果伙伴关系共同数据模型 (OMOP CDM) 数据的机器学习模型准确地预测计划入院的住院时间 (LoS). 这种方法提高了运营效率和患者护理协调.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床数据标准化标准化
背景情况:
- 准确预测住院时间 (LoS) 对于高效的医疗资源管理至关重要.
- 传统的LoS预测模型往往受到有限的共变量和非标准化数据的影响,阻碍了可重现性.
- 电子健康记录 (EHR) 数据的标准化对于开发可靠和可通用的预测模型至关重要.
研究的目的:
- 开发和验证基于机器学习 (ML) 的LoS预测模型,用于计划入院的患者.
- 利用观察医疗结果伙伴关系共同数据模型 (OMOP CDM) 进行标准化数据表示.
- 用ML算法评估预测性能并识别使用LoS预测的关键特征.
主要方法:
- 从137,437个计划入院 (2016-2020年) 的EHR数据的回顾性分析,转换为OMOP CDM v5.3.3.
- 包括来自患者,病情,药物,观察,测量,程序和访问发生表的各种共同变量.
- 开发6个ML模型,通过拉索规则化进行特征选择,并使用AUROC和AUPRC进行评估;在独立机构进行外部验证.
主要成果:
- 极端梯度增强 (XGB) 模型实现了最高的AUROC (0.891) 预测LoS≥7天.
- 光梯度增强 (LGB) 模型在LOS≥3天 (AUROC0.901) 的情况下表现最好.
- 关键预测指标包括程序,门诊访问频率,入院部门,年龄和入院日;随机森林模型显示了强大的外部验证 (AUROC 0.804).
结论:
- 使用OMOP CDM预测计划入院的医院LoS显示了各种持续时间的显著预测能力.
- 在OMOP CDM框架内的标准化数据有助于开发可重复和可靠的预测模型.
- 这种基于机器学习的标准化数据方法为改善医疗保健运营效率和患者护理协调提供了一个模型.
关键词:
欧洲人权理事会 欧洲人权理事会观察性医学结果伙伴关系 共同数据模型莎普利的附加式解释韩国 韩国 韩国 韩国接纳 接纳 接纳 接纳算法算法是一种算法.资源的分配和资源的分配.临床信息学 临床信息学电子健康记录 电子健康记录医疗保健 医疗保健 医疗保健医院 医院 医院 医院 医院停留时间的长度.逻辑回归的逻辑回归方法机器学习是机器学习.预测模型 预测模型结果的可复制性 结果的可复制性这是一项回顾性研究.更多相关视频
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