机器学习在住院患者坠床监测与风险评估中的应用研究进展

郑明亮, 肖志田, 罗书立, 余婷, 宁佩

护理学报 ›› 2025, Vol. 32 ›› Issue (13) : 28-31.

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护理学报 ›› 2025, Vol. 32 ›› Issue (13) : 28-31. DOI: 10.16460/j.issn2097-6569.2025.13.028
文献研究

机器学习在住院患者坠床监测与风险评估中的应用研究进展

  • 郑明亮1,2, 肖志田2, 罗书立2, 余婷2, 宁佩2
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摘要

目的 综述机器学习在住院患者坠床监测与风险评估的应用现状,为降低住院患者坠床伤害,提高住院安全,研发护理安全防护系统提供依据。方法 复习国内外相关文献,总结机器学习的数据采集方法的种类、坠床监测方法、风险评估工具以及应用效果,指出当前研究不足和应用的局限性。结果 传感器是构建机器学习数据库最常用的数据采集方式,当前有穿戴式、非接触式、混合式3种传感器;电子健康记录也是数据采集的方式之一;应用坠床监测与实时、非实时的风险评估,有助于临床降低坠床率和减轻坠床后伤害,护理人员应用满意度较高。结论 机器学习在住院患者坠床监测与风险评估中,具有个性化、敏感性及满意度高等特点,当前的临床应用中,存在传感器适用性不足、临床验证不足以及布置成本高等局限性。未来需开发新型低功耗、微型化传感器,深化多模态数据融合,开展临床实效研究,为护理安全实践提供智能化的支撑。

关键词

机器学习 / 坠床 / 住院患者 / 传感器 / 风险评估 / 护理

引用本文

导出引用
郑明亮, 肖志田, 罗书立, 余婷, 宁佩. 机器学习在住院患者坠床监测与风险评估中的应用研究进展[J]. 护理学报. 2025, 32(13): 28-31 https://doi.org/10.16460/j.issn2097-6569.2025.13.028
中图分类号: R472   

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基金

深圳市科技创新委员会基础研究面上项目(JCYJ20230807093803007)

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