机器学习在为手术患者开发和应用患者报告结果指标 (PROM):系统性审查
Tariq Alanezi1,2, Ben Li3,4,5,6, Leen Al-Omran7
1Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada. alanezitariq@gmail.com.
Journal of patient-reported outcomes
|January 14, 2026
概括
人工智能 (AI) 和机器学习 (ML) 可以在手术中增强患者报告的结果指标 (PROM). 这些技术提高了效率,减少了患者负担,并使手术结果的准确预测成为可能,优化了以患者为中心的护理.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 手术方面的创新.
- 医学中的数据科学.
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地融入医疗保健.
- 患者报告的结果测量 (PROM) 对于以患者为中心的外科护理至关重要.
- 人工智能/多发性肌痛为手术患者群体开发和应用PROM提供了潜在的进步.
研究的目的:
- 系统地审查AI和ML在手术群体的PROM中的使用.
- 评估AI/ML驱动的PROM的心理测量特性和预测准确性.
- 通过PROMs评估AI/ML在开发,应用和预测外科结果中的应用.
主要方法:
- 全面的PubMed数据库搜索到2024年8月.
- 包括使用AI/ML用于PROM开发,应用或预测手术患者的研究.
- 使用COSMIN和PROBAST工具进行方法质量评估,然后进行定性综合.
主要成果:
- 包括22项高质量的研究.
- AI/ML促进了计算机适应性测试 (CAT) PROMs的开发,提高了效率并减少了患者负担.
- 机器学习模型对术后结果的预测准确度相似,没有单一的模型始终优于其他模型.
结论:
- 人工智能和机器学习显示出提高PROM在外科护理中的利用的巨大潜力.
- 人工智能驱动的PROM可以降低患者负担,同时保持数据质量.
- 可以集成ML模型来准确地预测术后的结果,优化以患者为中心的护理.
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