机器学习模型包括瘤学中患者报告的结果数据:系统的文献审查和对其报告质量的分析
Daniela Krepper1, Matteo Cesari2, Niclas J Hubel3
1Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, University Hospital of Psychiatry II, Medical University of Innsbruck, Innsbruck, Austria. daniela.krepper@i-med.ac.at.
Journal of patient-reported outcomes
|November 5, 2024
概括
在癌症研究中使用患者报告结果测量 (PROM) 评分的机器学习 (ML) 模型显示报告质量较低. 未来的研究需要提高透明度和可重复性,以获得更好的临床应用.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 模型越来越多地用于癌症研究.
- 患者报告的结果指标 (PROM) 对于记录患者体验至关重要.
- 将ML与PROM数据整合在一起,有可能促进癌症治疗.
研究的目的:
- 在癌症研究中批判性地评估结合PROM分数的ML模型的报告质量.
- 发现有关ML模型报告的当前研究中的缺陷.
- 为未来的ML在瘤学中的应用提出改进建议.
主要方法:
- 在PubMed和Web of Science的系统文献搜索中使用ML与PROM分数进行癌症研究.
- 使用适应的MI-CLAIM检查清单评估报告质量,重点关注研究设计,数据,模型开发和检查.
- 对透明度和可重复性标准的评估.
主要成果:
- 在1634个符合条件的研究中,有52项符合条件的研究;69.2%使用PROM作为预测因素,61.5%作为结果.
- 机器学习模型的总体报告质量很低,有很大的改进空间,特别是在模型检查方面.
- 只有17.3%的人讨论了临床适用性和可重复性;5.8%提供了可重复的代码.
结论:
- 目前在瘤学中报告的ML模型与PROM得分是不理想的.
- 提高透明度,可重复性和模型检查对于癌症研究中强大的ML应用至关重要.
- 解决这些缺陷将提高ML在瘤学的临床效用和可靠性.
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