自然语言智能技术在慢性病护理中的有效性:系统性审查和元分析
Jiang Zeng1, Yuxin Xu2, Piaopiao Chen3
1Geriatric Medicine Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, 310014, China.
International journal of nursing studies
|March 13, 2026
概括
自然语言智能技术 (NLIT) 通过优化个性化护理和资源配置来改善慢性疾病管理. 尽管数据异质性和偏差存在挑战,但NLIT在各种应用中显示出显著的临床益处和高精度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- 慢性疾病对全球的健康构成重大负担.
- 由于使用非结构化患者数据进行持续护理的困难,管理慢性疾病是具有挑战性的.
- 自然语言智能技术 (NLIT) 为这些挑战提供了一个有希望的解决方案.
研究的目的:
- 系统地审查NLIT在治疗慢性疾病方面的有效性.
- 评估NLIT对临床结果和技术性能的影响.
- 确定在医疗保健中NLIT未来改进的挑战和领域.
主要方法:
- 按照PRISMA指南进行系统审查,该协议已在PROSPERO.com注册.
- 搜索了PubMed,科学网,Embase和Scopus (2014年1月至2024年1月) 这些网站.
- 使用NOS和QUADAS-C等级评估偏差风险.
主要成果:
- 六项研究 (28,323名参与者) 显示了NLIT在COPD管理 (27.9%的再入院减少),糖尿病护理 (改善自我管理,19.34%增加推合规) 和中风康复 (增强运动功能) 中的有效性.
- 技术性能包括88.46%的准确性预测COPD恶化和92%的诊断准确性糖尿病使用GPT-4.
- 超分析表明,尽管异质性 (I2=75%),但NLIT对临床结果有好处 (合并RR1.20,P=0.02).
结论:
- 对于个性化的慢性疾病护理和资源优化,NLIT非常有价值.
- 挑战包括数据异质性和偏差,需要进一步研究和改进.
- 通过NLIT,可以显著提高患者的治疗结果和医疗保健效率.
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