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相关概念视频

Current Trends in Nursing II01:30

Current Trends in Nursing II

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,...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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在护理研究中解码机器学习:对有效算法的范围审查.

Jeeyae Choi1, Hanjoo Lee2, Yeounsoo Kim-Godwin1

  • 1School of Nursing, College of Health and Human Services, University of North Carolina Wilmington, Wilmington, North Carolina, USA.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
|September 18, 2024
PubMed
概括

机器学习 (ML) 越来越多地用于护理研究,随机森林是最常见的算法. 建议进行进一步的研究,以优化在各种护理领域的ML模型使用.

关键词:
人工智能的人工智能是人工智能.机器学习是机器学习.机器学习算法的算法性能验证的验证性能验证的验证范围审查 范围审查审查

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科学领域:

  • 护理 护理 护理
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 人工智能 (AI) 正在迅速改变医疗保健,影响护理角色,并迫使研究人工智能集成系统.
  • 本范围审查专门审查了在护理领域的机器学习 (ML) 应用.

研究的目的:

  • 研究用于护理研究的机器学习 (ML) 算法.
  • 确定共同的模型评估方法和重点领域.
  • 确定护理中最有效的ML算法.

主要方法:

  • 根据PRISMA-ScR指南进行了范围审查.
  • 对七个主要数据库进行了系统搜索.
  • 通过医学教育研究研究质量工具 (MERSQI) 评估研究质量.

主要成果:

  • 审查了26篇文章 (2019-2023年),其中46%来自美国;平均MERSQI评分表明中等到高质量的研究.
  • 随机森林是最常用的ML算法,其次是后勤回归,LASSO,决策树和SVM.
  • 常见的评估指标包括灵敏度,特异性,准确性,ROC,AUROC和精度. 一半的研究集中在护理人员/学生和医院再接收/ED访问.

结论:

  • 机器学习 (ML) 对护士,护士从业人员和管理人员具有显著的临床相关性,证实了它在医疗保健中的好处.
  • 该审查强调了ML在护理研究中的日益重要以及它对患者护理和资源管理的影响.
  • 建议包括使用实验设计来优化在护理领域的ML模型应用.