在护理中利用机器学习:创新,挑战和道德见解
Sophie So Wan Yip1, Sheng Ning2, Niki Yan Ki Wong3
1School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, Hong Kong SAR, China.
Frontiers in digital health
|June 9, 2025
概括
护理中的机器学习 (ML) 提高了患者的护理,教育和效率. 伦理考虑和数据隐私仍然是未来集成的关键挑战.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 随着机器学习 (ML) 的整合,护士职业正在经历变革.
- 讨论护理中的ML对于职业进步至关重要.
- 这次审查平衡了技术创新与以人为中心的护理本质.
研究的目的:
- 综合分析机器学习 (ML) 在护理中的整合.
- 探索ML对患者护理,护理实践和医疗保健的影响.
- 突出目前的应用,挑战,伦理考虑,以及在护理中的ML的未来发展.
主要方法:
- 使用详细的搜索策略进行了叙事审查.
- 搜索的数据库包括PubMed,Embase,MEDLINE,Scopus和科学网络.
- 分析了2019年1月至2023年12月期间发表的文章,并将其分类为主题.
主要成果:
- 机器学习显著提高了患者监测,预测分析和预防护理,以改进的败血症预测模型为例.
- 在护士教育中的ML应用改善了基于模拟的培训和适应性学习.
- 通过自动化人员配置和减少行政任务,减少护士工作量,提高运营效率.
结论:
- 机器学习为护理提供了对患者护理,教育和运营效率的变革潜力.
- 重要的挑战,包括数据隐私和算法偏见等伦理考虑,需要持续关注.
- 未来的方向包括扩大应用,整合新技术,并通过跨学科合作加强护理教育.
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