在人口查中通过神经心理测试早期检测轻度认知障碍:一个整合本体学和机器学习的决策支持系统
Alba Gómez-Valadés1, Rafael Martínez-Tomás1, Sara García-Herranz2
1Department of Artificial Intelligence, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain.
Frontiers in neuroinformatics
|October 31, 2024
概括
这项研究引入了一种新的决策支持系统,使用机器学习和本体学来早期检测轻度认知障碍 (MCI). 该系统表现出与现有方法可比的性能,同时为临床使用提供了增强的可解释性和数据标准化.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 越来越多地用于轻度认知障碍 (MCI) 的检测.
- 机器学习算法的黑盒性质和数据异质性带来了解释挑战.
- 基于本体学的系统可以标准化数据并提高模型可解释性.
研究的目的:
- 设计一个决策支持系统,将ML与MCI检测的本体学集成在一起.
- 根据已建立的ML方法评估系统的性能.
- 在临床场景中展示系统的多功能性和易用性.
主要方法:
- 开发了一个结合ML模型 (SWRL) 与NIO本体学的决策支持系统.
- 在520个西班牙神经心理评估中对该系统进行了评估.
- 使用F2系数来优先考虑最大限度地减少假阴性.
主要成果:
- 该系统获得了0.830的F2得分,与其他ML方法 (例如,袋装在0.832) 相比.
- 该系统通过其本体学组件提供解释能力和数据标准化.
- 在处理不完整数据,整合新数据库和关联不同领域方面表现出多功能性.
结论:
- 拟议的系统有效地支持在人口查中早期发现MCI.
- 它的可解释性和标准化比传统的ML方法具有优势.
- 该系统是临床实践中医生和神经心理学家的一个有价值的工具.
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