基于患者临床记录的阿尔茨海默病诊断深度学习模型
J L Ávila-Jiménez1, Vanesa Cantón-Habas2, María Del Pilar Carrera-González3
1Departament of Electronic and Computer Engineering. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Córdoba, Spain.
Computers in biology and medicine
|December 19, 2023
概括
深度学习模型使用临床数据准确识别痴呆症患者的阿尔茨海默病 (AD). 这种人工智能工具在改善AD诊断方面表现有前途,特别是在诊断不足的神经退行性疾病方面.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 神经科学是一个神经科学.
背景情况:
- 痴呆症,特别是阿尔茨海默氏症 (AD),是老年人常见且未被确诊的疾病.
- 深度学习 (DL) 为开发基于AD风险因素的分类模型提供了一个有希望的方法,以解决不足诊断的问题.
研究的目的:
- 开发一个DL模型,使用临床数据对患有阿尔茨海默病的痴呆症患者进行分类.
- 通过计算方法提高AD诊断的准确性.
主要方法:
- 一个DL模型被设计用于从患者临床记录中识别AD.
- 数据集预处理涉及各种重新平衡技术.
- 对模型参数调整进行了广泛的研究.
主要成果:
- 与已建立的机器学习技术相比,开发的DL模型表现出卓越的性能.
- 该模型实现了具有统计学意义的曲线下面面积 (AUC) 值较高 (α < 0.05).
结论:
- 神经网络模型在AD诊断方面表现出强的性能.
- 这种人工智能工具可以作为诊断阿尔茨海默病的临床医生准确的辅助系统.
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