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使用系统性审查评估生物医学预测建模的变化
Lara Lusa1,2, Franziska Kappenberg3, Gary S Collins4
1Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper/Capodistria, Slovenia. lara.lusa@mf.uni-lj.si.
生物医学预测建模显示进化缓慢,机器学习的采用有限. 尽管参与者数量增加,但报告和验证的最佳实践仍然不足,突出需要改进标准.
科学领域:
- 生物统计学 生物统计学
- 生物医学信息学 生物医学信息学
- 流行病学 流行病学
背景情况:
- 生物医学预测模型的数量正在增加.
- 建议机器学习 (ML) 越来越多地用于复杂的数据结构和交互.
- 预测建模实践的变化需要审查.
研究的目的:
- 检查生物医学文献中预测建模实践的变化.
- 调查模型开发,验证和报告中的趋势.
- 评估采用机器学习技术的情况.
主要方法:
- 对2020-2022年期间发表的8篇评论进行系统审查.
- 包括至少50个预测模型的系统审查.
- 使用CHARMS检查清单从2005年开始发布的模型提取数据.
主要成果:
- 分析了887篇论文中的1448个预测模型.
- 参与者和活动数量增加到2019年,然后稳定.
- 内部验证和歧视报告有所改善,但校准和外部验证仍然很低.
- 机器学习的采用没有显著增加.
- 缺乏价值观的报告不足和对最佳实践的不充分遵守是常见的.
结论:
- 生物医学预测建模的变化不如预期那么剧烈.
- 遵守已建立的生物统计学最佳实践仍然是不够的.
- 在这个领域缺乏机器学习的最佳实践建议.
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