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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Updated: Jun 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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一个基于数据的框架,用于识别人工智能/机器学习模型可能表现不佳的患者子组.

Adarsh Subbaswamy1,2, Berkman Sahiner3, Nicholas Petrick3

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. adarsh.subbaswamy@fda.hhs.gov.

NPJ digital medicine
|November 21, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种算法框架,用于识别临床模型中潜在的性能差异的子组 (AFISP). 在部署之前,AFISP有助于在特定的患者群体中检测出较低的性能.

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相关实验视频

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科学领域:

  • 临床信息学是一种临床信息学.
  • 机器学习评估 机器学习评估
  • 健康数据科学健康数据科学

背景情况:

  • 在不同患者群体中评估临床模型至关重要.
  • 发展数据中的异质患者子组可以掩盖绩效差异.
  • 平均模型性能可能会隐藏特定子组的显著较低性能.

研究的目的:

  • 引入一种算法框架,用于识别潜在绩效差异的子组 (AFISP).
  • 为了能够检测与较低模型性能相关的可解释表型.
  • 为了在部署之前更容易识别潜在的临床模型故障模式.

主要方法:

  • 开发一种算法框架,用于识别潜在绩效差异的子组 (AFISP).
  • 产生可解释的表型,对应于具有较低模型性能的子组.
  • 将AFISP应用于患者病情恶化的模型.

主要成果:

  • AFISP成功地发现了大量的子组业绩差异.
  • 该框架产生可解释的表型,用于有针对性的评估.
  • 与现有方法相比,AFISP的可扩展性显著提高.

结论:

  • AFISP提供了一种可扩展和可解释的方法,用于检测临床模型中的性能差异.
  • 这一框架有助于识别特定患者子组的潜在故障模式.
  • AFISP提高了跨不同人群的临床模型评估的稳定性.