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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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个性化计算模型用于构建医疗数字双胞胎.

Adam C Knapp¹, Daniel A Cruz¹, Borna Mehrad¹

¹Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Florida.

bioRxiv : the preprint server for biology

|November 22, 2024

概括

此摘要是机器生成的。

我们开发了一个新的算法来个性化医疗保健中的计算模型. 这种方法使用数据同化来弥合患者数据的差距,从而实现更准确的个性化医疗预测.

关键词:

基于代理的模型基于代理的模型数据同化数据同化整体卡尔曼过器的组合医疗数字双胞胎医疗数字双胞胎

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科学领域:

生物医学工程生物医学工程
计算生物学是一种计算生物学.
数字健康数字健康

背景情况:

数字双胞胎技术最初用于工程,现在应用于生物医学.
用患者数据来个性化计算模型对于预测性医疗保健至关重要.
目前的方法难以将宏观临床数据与微观模型要求相结合.

研究的目的:

开发一种新的算法,用于动态校准特定患者的计算模型.
解决在个性化医疗中将宏观状态 (临床数据) 和微观状态 (模型参数) 联系在一起的挑战.
提高医疗保健应用中基于模型的预测的准确性.

主要方法:

在宏观状态层面应用集体卡尔曼波器的算法.
将宏观状态更新链接到基于代理模型的微观状态更新.
确保的微态与宏态兼容,并且可能根据模型动态.

主要成果:

该算法成功地弥合了临床测量和细粒度模型数据之间的差距.
生成的微态与所需的宏态和模型动态兼容.
为个性化复杂的生物医学模型提供了一种新的方法.

结论:

开发的算法提供了一个强大的方法来个性化医疗保健中的基于代理的模型.
这种方法可以为个性化医学提供更准确的预测.
能够更好地利用患者数据进行动态模型校准.