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Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

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
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Updated: Jul 20, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

在医疗机器学习中实现平等绩效的道路

Eike Petersen^1,2, Sune Holm^2,3, Melanie Ganz^2,4,5

¹DTU Compute, Technical University of Denmark, Richard Pedersens Plads, 2800 Kgs. Lyngby, Denmark.

Patterns (New York, N.Y.)

|July 31, 2023

概括

此摘要是机器生成的。

跨患者群体的机器学习模型性能差异阻碍了公平的护理. 解决这些问题需要了解代表性不足和任务难度,不仅仅是更多的数据,而是从表现不佳的群体获得更好的数据.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

相关实验视频

Last Updated: Jul 20, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

科学领域:

医疗保健中的机器学习
健康公平卫生公平
医疗信息学医疗信息学

背景情况:

患者群体之间的机器学习模型性能差异可能会损害公平的护理质量.
两个主要机制导致了这些绩效差异:相对于理论上的最大值,绩效低于最佳,以及各组预测任务难度的内在差异.

研究的目的:

阐明导致不同患者群体在机器学习模型中的性能差异的独特机制.
分析小组代表性不足和任务特征对模型低绩效的影响.
探索导致患者群体之间不同最佳可实现性能水平的因素.

主要方法:

检查代表性不足导致业绩不足的场景,与没有导致业绩不足的场景相比.
讨论预测任务难度内在差异的潜在原因.
在模型学习和评估中分析混因素,如标签和选择偏见.

主要成果:

代表性不足,建模选择和任务特征可能导致模型性能比特定群体中理论上可以实现的要差.
预测任务的内在难度的差异可以导致不同组的最佳可实现性能水平变化.
标签和选择偏见可以显著混学习过程和模型性能评估.

结论:

为了实现公平的机器学习模型性能,需要针对表现不佳的群体解决数据量和质量问题.
"升级"模型性能的策略必须考虑数据表示,建模决策和任务特定挑战的微妙相互作用.
缓解偏见和理解任务难度对于通过机器学习推进健康公平至关重要.