基于患者临床记录的阿尔茨海默病诊断深度学习模型。
A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.
机构信息
Departament of Electronic and Computer Engineering. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Córdoba, Spain.
Maimonides Institute for Biomedical Research, Reina Sofia University Hospital, University of Córdoba, Spain.
出版信息
Comput Biol Med. 2024 Feb;169:107814. doi: 10.1016/j.compbiomed.2023.107814. Epub 2023 Dec 9.
BACKGROUND
Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis.
OBJECTIVE
To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD.
METHODS
A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model.
RESULTS
Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05.
CONCLUSIONS
The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.
背景
痴呆症是一种神经退行性疾病，其中阿尔茨海默病（AD）最为常见，是老年人中诊断不足的健康问题。使用深度学习技术基于 AD 风险因素创建分类模型是一种很有前途的工具，可以最大程度地减少诊断不足的影响。
目的
开发一种基于患者的痴呆症临床数据的深度学习模型，以对其是否患有 AD 进行分类。
方法
提出了一种用于在临床记录中识别 AD 的深度学习模型。此外，还使用了几种重新平衡方法来预处理数据集，并进行了多项研究来调整模型。
结果
与其他成熟的机器学习技术相比，该模型的 AUC 指标更高，具有更好的性能，在 alpha 小于 0.05 的情况下，其表现优于这些技术。
结论
所开发的神经网络模型具有良好的性能，可以成为 AD 诊断的准确辅助工具。
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