通过传统和机器学习方法,协调患者报告的鼻子投诉的结果指标
Miljan Jović1, Esther Hof2, Maryam Amir Haeri1
1Department of Learning, Data Analytics and Technology, University of Twente, Enschede, the Netherlands.
International journal of medical informatics
|February 6, 2026
概括
机器学习方法最好地协调鼻腔阻塞数据,优于传统的物件响应理论. 这提高了鼻腔外科研究中不同测量仪器的得分可比性.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗信息学 医疗信息学
- 心理测量 心理测量 心理测量
背景情况:
- 鼻腔阻塞的评估依赖于具有不同尺度的多种测量仪器.
- 数据协调对于比较不同仪器的分数至关重要,但未得到充分利用.
- 标准化鼻腔阻塞数据增强了研究和临床分析.
研究的目的:
- 确定协调来自多种仪器的鼻塞得分的最佳方法.
- 开发一种转换方法,以确定NOSE,Utrecht-Q和SCHNOS尺度与ENFAS尺度之间的等价性.
- 为了提高不同鼻腔投诉仪器之间的得分预测的精度.
主要方法:
- 比较线性等式,物件响应理论 (IRT) 和机器学习 (ML) 方法.
- 利用线性回归,随机森林,支持矢量机器和神经网络进行协调.
- 在5倍交叉验证中使用根平均平方误差 (RMSE) 评估协调质量,对1324名患者进行了交叉验证.
主要成果:
- 机器学习方法在RMSEs最低的情况下产生了优异的协调结果.
- ML的方法超过了IRT,一种常见的心理测量协调技术.
- 所有测试的ML模型都在得分预测和协调方面表现出有效性.
结论:
- 机器学习方法为鼻腔阻塞评估提供高质量的数据协调.
- ML方法为线性等效和IRT提供了一个强大的替代方案,用于协调鼻腔投诉得分.
- 实施ML可以在特定的鼻腔手术研究场景中显著提高数据协调质量.
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