摘要
目的系统评价机器学习在老年人跌倒预测和监测中的应用。方法采用主题词与自由词相结合的方式,计算机检索中国知网、中国生物医学文献数据库(SinoMed)、维普期刊数据库、万方数据库、Web of Science、PubMed、Embase和CINAHL,检索时限为建库至2022年7月。由2名研究者独立对纳入研究提取关键信息并进行总结,选择使用诊断试验偏倚风险评价工具(Quality Assessment of Diagnostic Accuracy Studies-2,QUADAS-2)对纳入研究进行偏倚风险和合适性评价。结果纳入15篇研究,模型和算法大多选择神经网络和随机森林;模型预测目的多数未细化人群,少数研究关注特定人群,如骨质疏松老年女性、急诊老年患者等;质量评价结果显示,总体而言,纳入研究偏倚风险较低、适用性强。结论机器学习在老年人跌倒识别与风险预测中,具有较高的预测能力和应用价值,但仍存在部分问题,大数据分析仍需扎实的临床经验作为基础,并不断细化和区分特殊人群,使人工智能在护理领域中的应用得以发展。
Objective To systematically review the fall prediction and monitor value of machine learning (ML) for older adults. Methods Databases including CNKI,SinoMed,VIP,Wanfang Data,Web of Science,PubMed,Embase and CINAHL were searched to retrieve all studies that focused on ML in predicting fall of older adults. The searching time was set from the date of database's establishment to July 2022. After that,two reviewers independently screened literature,extracted data and assessed the risk of bias of included studies by the standard of QUADAS-2. Results A total of 15 studies were included. In most studies,neural network and random forest models were mainly being used. In addition,though a few studies focused on specific populations,such as older women with osteoporosis and older patients in acute care,the majority of studies included older adults. Based on the quality evaluation,the included studies were highly applicable and had a low risk of bias. Conclusion ML has high predictive value and application value in fall identification and fall risk prediction for older adults. Meanwhile,there are still some problems. In the future,big data analysis should still base on clinical experience,as well as pay attention to specific populations,in order to develop a deeper combination and development of artificial intelligence and nursing.
作者
罗园
张华
张孟喜
邓雨茜
冉海烨
刘佳欣
赵丽萍
LUO Yuan;ZHANG Hua;ZHANG Mengxi;DENG Yuqian;RAN Haiye;LIU Jiaxin;ZHAO Liping
出处
《中华护理教育》
CSCD
北大核心
2023年第12期1513-1519,共7页
Chinese Journal of Nursing Education
基金
湖南省自然科学基金(2021JJ70068)
湖南省市场监督局2022年度标准化项目。
关键词
机器学习
跌倒
老年人
系统评价
风险预测
Machine learning
Falls
Older adults
Systematic review
Risk prediction
