应用机器学习来增强核心成果集开发:自动化数据提取和结果分类
Ali Yalcinkaya1,2, Kristian Gade Kjelmann3, Shima Gholinezhad4
1Interdisciplinary Orthopaedics, Department of Orthopaedic Surgery, Aalborg University Hospital, Aalborg, Denmark. a.yalcinkaya@rn.dk.
Journal of orthopaedic surgery and research
|November 7, 2025
概括
机器学习模型可以准确地提取和分类临床结果,大大减少开发核心结果集所需的数据. 这加快了证据综合和达成共识,为标准化研究报告加快了时间.
科学领域:
- 临床研究方法论临床研究方法论
- 医疗信息学 医疗信息学
- 生物医学数据科学是生物医学数据科学.
背景情况:
- 核心成果集 (COS) 标准化了报告,但需要大量的劳动力来开发.
- 手动从文献中提取和分类结果是耗时的.
- 对于COS开发的机器学习 (ML) 应用尚未得到充分探索.
研究的目的:
- 评估ML模型,以便在COS开发中准确地提取和分类结果.
- 确定可靠的ML性能所需的最小手动注释数据.
- 评估ML在下肢延长手术的可行性.
主要方法:
- 开发了一个ML管道与句子-BERT用于结果提取和COMET分类学分类模型.
- 利用了149项关于下肢延长手术的全文研究的数据集.
- 用5至85件的训练套装进行性能评估,并在28件的持有套装上进行验证.
主要成果:
- 稳定的ML模型性能仅在20篇注释文章中实现.
- 提取模型:94%的F1得分;分类模型:86%的加权平均F1得分.
- ML系统识别了94%的手动提取的结果,具有很高的准确性.
结论:
- 基于ML的结果提取和分类对于COS开发是可行的.
- 减少注释需求,从149篇文章减少到20篇,同时保持准确性.
- 提供了一个可扩展,可重复的解决方案,以减少证据合成中的手工工作量.
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