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相关概念视频

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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Documentation of Nursing Diagnosis01:10

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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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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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
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人工智能:预测外科手术问题

Joseph Harris1, James Matthews1

  • 1Department of Anaesthesia, Royal London Hospital, London, UK.

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|August 30, 2024
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概括
此摘要是机器生成的。

人工智能 (AI) 算法在预测和建模外科手术后并发症方面表现有前途,可能会超过传统方法. 然而,范围和透明度有限等挑战阻碍了广泛的临床采用,并影响了患者的治疗结果.

关键词:
麻醉 anesthesia 这是一种麻醉.人工智能的人工智能是人工智能.数据科学数据科学机器学习是机器学习.在外科手术期间的医学.风险预测风险预测

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科学领域:

  • 医疗信息学 医疗信息学
  • 临床决策支持 临床决策支持
  • 人工智能在医学中的应用

背景情况:

  • 人工智能 (AI) 正在迅速发展,为临床医生提供潜在的工具.
  • 人工智能算法正在开发中,以建模和预测外科手术期间的并发症.
  • 目前的风险预测工具在准确性和范围上有局限性.

研究的目的:

  • 概述人工智能在外科手术期间风险分层和外科手术期间事件预测中的新兴应用.
  • 将人工智能算法的性能与传统风险预测工具进行比较.
  • 讨论AI在临床实践中的挑战和潜在影响.

主要方法:

  • 审查在手术前风险分层中新兴的人工智能应用.
  • 分析人工智能算法用于手术内事件预测.
  • 人工智能性能与既有风险预测方法的比较.

主要成果:

  • 人工智能算法在风险分层和事件预测方面的表现已被证明超过了传统工具.
  • 人工智能展示了高精度建模和预测术后问题的潜力.
  • 新兴的人工智能应用程序为外科手术期间的护理提供了一种新的方法.

结论:

  • 人工智能为通过预测能力增强外科手术期间护理提供了一个有希望的未来.
  • 人工智能采用的关键挑战包括其有限的范围和缺乏透明度.
  • 机器学习对临床实践和患者治疗结果的影响尚未完全确定.