PDF(Pubmed)
Abstract：
UNASSIGNED: Mental disorders (MDs) are becoming a leading burden in non-communicable diseases (NCDs). As per the World Health Organization\'s 2022 assessment report, there was a steep increase of 25 % in MDs during the COVID-19 pandemic. Early diagnosis of MDs can significantly improve treatment outcome and save disability-adjusted life years (DALYs). In recent times, the application of machine learning (ML) and deep learning (DL)) has shown promising results in the diagnosis of MDs, and the field has witnessed a huge research output in the form of research publications. Therefore, a bibliometric mapping along with a review of recent advancements is required.
UNASSIGNED: This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed.
UNASSIGNED: The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka\'s law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.
UNASSIGNED: This study presents a bibliometric analysis and review of the research, published over the last 10 years. Literature searches were conducted in the Scopus database for the period from January 1, 2012, to June 9, 2023. The data was filtered and screened to include only relevant and reliable publications. A total of 2811 journal articles were found. The data was exported to a comma-separated value (CSV) format for further analysis. Furthermore, a review of 40 selected studies was performed.
UNASSIGNED: The popularity of ML techniques in diagnosing MDs has been growing, with an annual research growth rate of 17.05 %. The Journal of Affective Disorders published the most documents (n = 97), while Wang Y. (n = 64) has published the most articles. Lotka\'s law is observed, with a minority of authors contributing the majority of publications. The top affiliating institutes are the West China Hospital of Sichuan University followed by the University of California, with China and the US dominating the top 10 institutes. While China has more publications, papers affiliated with the US receive more citations. Depression and schizophrenia are the primary focuses of ML and deep learning (DL) in mental disease detection. Co-occurrence network analysis reveals that ML is associated with depression, schizophrenia, autism, anxiety, ADHD, obsessive-compulsive disorder, and PTSD. Popular algorithms include support vector machine (SVM) classifier, decision tree classifier, and random forest classifier. Furthermore, DL is linked to neuroimaging techniques such as MRI, fMRI, and EEG, as well as bipolar disorder. Current research trends encompass DL, LSTM, generalized anxiety disorder, feature fusion, and convolutional neural networks.
摘要：
精神障碍（MD）正在成为非传染性疾病（NCDs）的主要负担。根据世界卫生组织的2022年评估报告，在COVID-19大流行期间，MD急剧增加了25%。早期诊断MD可以显着改善治疗结果并节省残疾调整生命年（DALYs）。最近,机器学习(ML)和深度学习(DL)的应用在MD的诊断中显示出了有希望的结果，该领域以研究出版物的形式见证了巨大的研究成果。因此,需要进行文献计量学制图以及对最新进展的回顾。
■本研究对研究进行了文献计量分析和综述，发表在过去10年。2012年1月1日至2023年6月9日期间，在Scopus数据库中进行了文献检索。数据经过过滤和筛选，仅包括相关和可靠的出版物。共发现2811篇期刊文章。将数据导出为逗号分隔值(CSV)格式以供进一步分析。此外，我们对40项选定的研究进行了回顾.
■ML技术在诊断MD方面的普及程度一直在增长，年研究增长率为17.05%。情感障碍杂志发表的文献最多(n=97)，而王勇(n=64)发表的文章最多。洛特卡的定律被遵守，少数作者贡献了大多数出版物。最大的附属机构是四川大学华西医院，其次是加州大学，中国和美国占据了前10名的研究所。虽然中国有更多的出版物，与美国相关的论文获得了更多的引用。抑郁症和精神分裂症是ML和深度学习（DL）在精神疾病检测中的主要关注点。共现网络分析显示，ML与抑郁症有关，精神分裂症,自闭症,焦虑,多动症,强迫症,PTSD流行的算法包括支持向量机(SVM)分类器,决策树分类器，和随机森林分类器。此外，DL与MRI等神经成像技术有关，功能磁共振成像,和脑电图，以及双相情感障碍。当前的研究趋势包括DL，LSTM,广泛性焦虑障碍,特征融合,和卷积神经网络。
■本研究对研究进行了文献计量分析和综述，发表在过去10年。2012年1月1日至2023年6月9日期间，在Scopus数据库中进行了文献检索。数据经过过滤和筛选，仅包括相关和可靠的出版物。共发现2811篇期刊文章。将数据导出为逗号分隔值(CSV)格式以供进一步分析。此外，我们对40项选定的研究进行了回顾.
■ML技术在诊断MD方面的普及程度一直在增长，年研究增长率为17.05%。情感障碍杂志发表的文献最多(n=97)，而王勇(n=64)发表的文章最多。洛特卡的定律被遵守，少数作者贡献了大多数出版物。最大的附属机构是四川大学华西医院，其次是加州大学，中国和美国占据了前10名的研究所。虽然中国有更多的出版物，与美国相关的论文获得了更多的引用。抑郁症和精神分裂症是ML和深度学习（DL）在精神疾病检测中的主要关注点。共现网络分析显示，ML与抑郁症有关，精神分裂症,自闭症,焦虑,多动症,强迫症,PTSD流行的算法包括支持向量机(SVM)分类器,决策树分类器，和随机森林分类器。此外，DL与MRI等神经成像技术有关，功能磁共振成像,和脑电图，以及双相情感障碍。当前的研究趋势包括DL，LSTM,广泛性焦虑障碍,特征融合,和卷积神经网络。
