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相关概念视频

Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

559
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
559
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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相关实验视频

Updated: May 28, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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对30天儿科医院再接收风险预测模型的验证.

Alison R Carroll1,2, Matthew Hall3, Mitch Harris3

  • 1Division of Pediatric Hospital Medicine, Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee.

JAMA network open
|February 13, 2025
PubMed
概括

儿科再接收风险模型显示,随着时间的推移,准确性下降,医院的表现各不相同. 在临床使用之前,本地验证至关重要,以确保可靠的预测,避免可预防的医院再入院.

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科学领域:

  • 儿科医疗保健研究的研究.
  • 临床信息学是一种临床信息学.
  • 医疗服务研究 医疗服务研究

背景情况:

  • 准确识别医院再接收风险有助于决策和有针对性的干预措施.
  • 可预防的再接收对医疗保健系统构成重大负担.

研究的目的:

  • 在多家医院验证儿童再接收风险预测模型.
  • 评估这些模型对临床实施的概括性和可行性.

主要方法:

  • 使用48家美国儿童医院的儿科健康信息系统 (PHIS) 数据库进行预后研究.
  • 分析了2016-2019年三种不同儿科队列的数据:新入院模型 (NAM),最近入院模型 (RAM) 和幼婴模型 (YIM).
  • 使用接收器运行特征曲线下面面积 (AUROC) 和校准图表的模型的时间和外部验证.

主要成果:

  • 与原始估计相比,时间验证显示所有模型的歧视减少.
  • 外部验证显示了类似的趋势,在各医院的表现中存在显著差异.
  • 大多数医院的校准表现不佳,并高估或低估了再接收风险.

结论:

  • 再接收风险预测模型显示,随着时间的推移,准确性降低,各机构的表现变化.
  • 在将这些模型应用于临床实践之前,必须进行本地验证.
  • 提高通用性可能需要多中心模型衍生和更广泛的预测器集.