协作过用于患者报告结果的推算
Eric Ababio Anyimadu1, Clifton David Fuller2, Xinhua Zhang3
1Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
概括
这项研究引入了协作过,以填补头癌患者缺少的患者症状数据. 这种方法证明优于其他归算技术,用于改进患者报告的结果数据集.
科学领域:
- 在瘤学瘤学.
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 缺少数据是患者报告结果 (PRO) 数据集中的一个重大挑战.
- 准确的数据归算对于医疗保健中的机器学习和数据挖掘至关重要.
- 在临床数据集中,头癌的症状评级往往不完整.
研究的目的:
- 建议和评估协作过用于指责头癌患者缺失的症状评级.
- 为了比较协作过与已建立的归算方法的有效性.
- 提高患者报告结果数据的可用性,以便进一步分析.
主要方法:
- 利用了两个协作过方法:基于患者和基于症状的归算.
- 将协作过性能与通过链式方程的多重推算,近邻推算和线性插值进行比较.
- 使用根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 度量来评估归算准确性.
主要成果:
- 协作过在赋予缺失的症状评级方面表现强.
- 基于患者和基于症状的协作过是有效的归算策略.
- 协作过的性能优于传统的方法,如通过链式方程的多重推算和近邻推算.
结论:
- 协作过是一种可行且优越的方法,用于归因失踪的患者症状数据.
- 这种方法可以显著提高头癌PRO数据集的质量.
- 这些发现支持在临床数据预处理管道中集成协作过.
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