摘要
背景:帕金森病是第二大常见的神经退行性疾病。其诊断具有挑战性,且依赖于临床方面。目前,没有生物标志物可用于在体内获得确定性的诊断。 目标:本综述旨在描述机器学习算法,该方法已不同程度地应用于帕金森病诊断和表征的不同方面。 方法:2019 年 12 月在 PubMed 上进行了系统搜索,通过以下搜索查询获得了 230 篇出版物:“机器学习”“AND”“帕金森病”。 结果:获得的出版物根据不同的应用领域分为6类:“步态分析-运动评估”、“上肢运动和震颤评估”、“手写和打字评估”、“言语和发声评估”、“神经影像学”和核医学评价”,“代谢组学应用”,排除一般主题的论文。结果,剔除以英语以外的语言撰写的论文或与所选主题不直接相关的论文后,共分析了 166 篇论文。 结论:机器学习算法是基于计算机的统计方法,可以训练并能够从大量数据中找到常见模式。机器学习方法可以帮助临床医生同时根据几个变量对患者进行分类。
关键词: 机器学习、帕金森病、代谢组学、步态分析、神经影像学、语音分析、笔迹分析。
Current Medicinal Chemistry
Title:Machine Learning Approaches in Parkinson’s Disease
Volume: 28 Issue: 32
关键词: 机器学习、帕金森病、代谢组学、步态分析、神经影像学、语音分析、笔迹分析。
摘要:
Background: Parkinson’s disease is the second most frequent neurodegenerative disorder. Its diagnosis is challenging and mainly relies on clinical aspects. At present, no biomarker is available to obtain a diagnosis of certainty in vivo.
Objective: The present review aims at describing machine learning algorithms as they have been variably applied to different aspects of Parkinson’s disease diagnosis and characterization.
Methods: A systematic search was conducted on PubMed in December 2019, resulting in 230 publications obtained with the following search query: “Machine Learning” “AND” “Parkinson Disease”.
Results: The obtained publications were divided into 6 categories, based on different application fields: “Gait Analysis - Motor Evaluation”, “Upper Limb Motor and Tremor Evaluation”, “Handwriting and typing evaluation”, “Speech and Phonation evaluation”, “Neuroimaging and Nuclear Medicine evaluation”, “Metabolomics application”, after excluding the papers of general topic. As a result, a total of 166 articles were analyzed after elimination of papers written in languages other than English or not directly related to the selected topics.
Conclusion: Machine learning algorithms are computer-based statistical approaches that can be trained and are able to find common patterns from big amounts of data. The machine learning approaches can help clinicians in classifying patients according to several variables at the same time.
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Cite this article as:
Machine Learning Approaches in Parkinson’s Disease, Current Medicinal Chemistry 2021; 28 (32) . https://dx.doi.org/10.2174/0929867328999210111211420
DOI https://dx.doi.org/10.2174/0929867328999210111211420 |
Print ISSN 0929-8673 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-533X |
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