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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.
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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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开发基于机器学习的远程医疗的自动化患者参与估计器:算法开发和验证研究

Pooja Guhan1, Naman Awasthi1, Kathryn McDonald2

  • 1Department of Computer Science, University of Maryland, College Park, MD, United States.

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概括
此摘要是机器生成的。

机器学习模型现在可以估计患者参与远程医疗,改善治疗联盟. 这项技术通过在虚拟心理健康会议期间提供可靠的参与指标来协助心理治疗师.

关键词:
接触检测 接触检测 接触检测机器学习是机器学习.心理健康 心理健康患者参与度 患者参与度远程医疗服务是远程医疗服务.

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科学领域:

  • 心理学和机器学习
  • 行为健康护理 技术 技术
  • 远程医疗创新 远程医疗创新

背景情况:

  • 患者参与在行为健康方面至关重要,但由于有限的非语言线索,在远程医疗方面具有挑战性.
  • 现有的远程医疗患者参与培训很少,需要新的评估方法.
  • 机器学习为估计虚拟治疗期间患者参与度提供了一个潜在的解决方案.

研究的目的:

  • 评估机器学习模型估计患者在远程心理健康中的参与水平的能力.
  • 确定机器学习是否可以支持和增强客户和心理治疗师之间的治疗参与.
  • 引入一个新的数据集,以推进远程医疗参与检测研究.

主要方法:

  • 开发了一种多式学习方法,利用情感和认知参与特征的潜在载体.
  • 由于医疗保健中的标记数据限制,研究了一种半监督学习解决方案.
  • 在临床分析中多模式参与检测 (MEDICA) 数据集,包括1229个视频剪辑,创建并用于实验.

主要成果:

  • 与最先进的方法相比,拟议的算法在参与估计的根平均平方误差中实现了40%的改进.
  • 现实世界的测试显示,模型的参与估计和心理治疗师的工作联盟清单分数之间存在正相关性.
  • 这些发现表明该模型有可能提供与临床评估一致的患者参与估计.

结论:

  • 机器学习可以准确可靠地估计患者参与远程医疗,支持治疗联盟.
  • 开发的算法将心理理论与机器学习相结合,用于增强远程医疗患者参与度评估.
  • 创建MEDICA数据集和提出的方法为远程医疗工具开辟了新的研究途径.