预测积累成功为更好的临床试验资源分配分配
Sisi Ma1,2, Yinzhao Wang3, John Wagner4
1Institute for Health Informatics, University of Minnesota, Minneapolis, MN, 55455, USA. sisima@umn.edu.
Scientific reports
|January 31, 2025
概括
现在可以预测由于患者积累不良而导致的临床试验失败. 机器学习模型准确地预测试验成功,防止资源浪费,提高临床试验效率.
科学领域:
- 临床试验的管理管理.
- 生物医学数据科学是生物医学数据科学.
- 在医疗保健中的预测分析.
背景情况:
- 临床试验的成功严重依赖于患者的积累.
- 大约55%的终止试验由于患者招募不足而失败.
- 低的积累率导致了重大的财务和社会成本.
研究的目的:
- 开发和验证临床试验失败的预测模型,这些失败是由于积累不良造成的.
- 为了能够及早识别可能无法达到入学目标的试验.
- 为资源分配提供信息,提高临床试验的整体成功率.
主要方法:
- 从clinicaltrials.gov (57,846个试验) 构建一个大型数据集.
- 使用文献审查和自然语言处理的特征工程.
- 应用先进的监督机器学习算法进行预测.
- 模型验证使用交叉验证和前性测试.
主要成果:
- 开发了具有强大的预测性能的模型 (交叉验证AUC=0.744,预期AUC=0.737).
- 在10年的时间里表现出稳定的业绩.
- 改进了模型校准,并探索了拒绝选项对于决策支持的实用性.
结论:
- 该研究介绍了使用全面数据集和先进机器学习预测临床试验积累失败的第一个模型.
- 这些模型为优化临床试验规划和资源配置提供了有价值的决策支持.
- 开发的工具可以提高临床研究工作的效率和成功率.
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