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Current Trends in Nursing II01:30

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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 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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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.
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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基于人工智能的临床预测模型的更新方法:范围审查

Lotta M Meijerink1, Zoë S Dunias1, Artuur M Leeuwenberg1

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

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概括

本研究审查了基于人工智能 (AI) 的临床预测模型与新数据更新的方法. 它强调了各种技术,主要关注神经网络,以适应模型用于各种医疗保健应用.

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人工智能的人工智能是人工智能.知识转移知识转移.机器学习是机器学习.更新模型的更新.预测模型的预测模型.转移学习转移学习

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科学领域:

  • * 医疗人工智能 (AI) 是一种人工智能.
  • * * 临床预测建模
  • * 医疗保健中的机器学习

背景情况:

  • *基于人工智能的预测模型在医疗保健中至关重要,但可能无法将其推广到新的环境中.
  • *为每个环境开发新的模型是低效和浪费的.
  • * 更新现有的人工智能模型为提高性能和资源利用提供了实际解决方案.

研究的目的:

  • * 为更新基于AI的临床预测模型提供方法的全面概述.
  • * 分类和描述现有的模型更新技术及其用例.
  • *指导研究人员将AI模型适应新数据和临床情景.

主要方法:

  • * 截至2022年8月,对Scopus和Embase进行全面的文献搜索.
  • *专注于基于人工智能的预测模型,并与医学领域的新数据进行更新.
  • *不包括基于回归的更新方法;分类识别的AI更新方法.

主要成果:

  • *包括78篇文章,主要关注神经网络更新 (93.6%),使用医学图像 (65.4%).
  • * 常见的用例包括将广义模型适应专业任务,处理数据漂移和处理跨中心变化.
  • *识别的方法分为神经网络特异性 (92.3%),集合特异性 (2.5%) 和模型不可知性 (9.0%).

结论:

  • *有许多方法可以在各种用例中更新基于AI的预测模型.
  • * 对非神经网络AI模型 (例如随机森林) 的更新方法在临床环境中研究不足.
  • * 本综述作为指导,以提高人工智能模型在医疗保健中的再利用,质量和效率.