图表神经网络用于帕金森病监测和警报
Nikolaos Zafeiropoulos1, Pavlos Bitilis1, George E Tsekouras1
1Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
图形神经网络 (GNN) 为了解帕金森病 (PD) 的复杂性提供了一个强大的工具. 本综述探讨了GNN在PD研究中的当前应用,强调了未来的方向和一种新的监测方法.
科学领域:
- * 计算神经科学和机器学习在神经退行性疾病中的应用.
- * 人工智能在医疗保健和医疗信息学中.
背景情况:
- *帕金森病 (PD) 是一种复杂的神经退行性疾病,具有多因素进展.
- *图形神经网络 (GNN) 越来越多地用于在PD研究中建模复杂的关系.
- *了解这些关系对于有效的PD监测和管理至关重要.
研究的目的:
- *为帕金森病研究中最先进的GNN应用提供全面的审查.
- * 介绍使用GNN在PD中的动机,背景和方法.
- * 确定基于GNN的PD研究中的当前挑战和未来研究途径.
主要方法:
- * 系统的文献审查,遵循PRISMA指南.
- *分析各种GNN架构及其在PD相关任务中的性能.
- * 识别和讨论现有的PDGN方法的局限性.
主要成果:
- * 在帕金森病研究中使用的各种GNN类型的概述.
- *从GNN应用程序中获得的结果和性能指标的总结.
- * 确定目前基于GNN的PD研究中的未解决的问题和挑战.
结论:
- *GNN在模拟复杂的PD因子方面显示出显著的前景.
- *需要进一步的研究来克服局限性并增强GNN的实用性.
- * 提出了一种新的方法,将GNN在PD监测和警报中整合新的任务.
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