数据驱动的算法用于在丹麦国家患者登记册中的室内和门诊患者的分类
Ann-Sophie Buchardt1, Pi Vejsig Madsen1, Andreas Jensen1,2
1Mary Elizabeth's Hospital, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Clinical epidemiology
|February 26, 2025
概括
研究人员开发了算法,在关键变量被删除后,在丹麦国家患者登记册 (DNPR3) 中对患者类型进行分类. 这些方法准确地分类了医院联系人,确保了丹麦注册研究数据的一致解释.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生数据管理公共卫生数据管理
- 生物统计学 生物统计学
背景情况:
- 丹麦国家患者登记册 (DNPR) 是健康研究的重要资源.
- 过渡到DNPR3删除了患者类型变量 (住院患者,门诊患者),影响了数据的一致性.
- 在DNPR3中缺乏患者类型分类,这阻碍了对医院接触者的可靠分析.
研究的目的:
- 在DNPR3.3中提出和评估将医院接触者分为住院患者,选择性门诊患者和急性门诊患者的算法.
- 确保使用丹麦健康登记册的研究人员在数据解释方面达成共识.
- 为了解决数据分类挑战,DNPR3.3中删除了患者类型变量.
主要方法:
- 2017-2020年使用DNPR2和DNPR3数据分析丹麦公共医院的体质接触.
- 开发和评估基于部门,基于联系和混合分类算法.
- 由于缺乏真实患者类型数据,在DNPR3中使用了代理标签用于培训分类模型.
主要成果:
- 基于部门的分类器在DNPR2中显示了高的正预测值 (PPV) 和灵敏度 (PPV 95.6-99.5,灵敏度 94.1-99.6).
- 混合方法改善了急性 (97.3,96.8) 和选择性 (99.8,99.9) 门诊患者的PPV和敏感性.
- 在DNPR2和DNPR3中,基于接触的算法之间的高度一致性表明了强大的和固有的数据模式.
结论:
- 提出的分类方法适用于DNPR2和DNPR3.3中的患者类型的分类.
- 分类方法的稳定性支持它们对DNPR3数据的适用性.
- 未来的研究应该专注于先进的技术和全面的部门分类,以提高准确性.
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