使用机器学习诊断精神障碍:文献综述和从2012年到2023年的文献识别映射
Chandra Mani Sharma1,2, Vijayaraghavan M Chariar1
1CRDT, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Heliyon
|July 8, 2024
概括
机器学习和深度学习在诊断精神障碍方面越来越有前途. 这项研究绘制了研究趋势,确定了关键算法和重点领域,如抑郁症和精神分裂症,以改善早期检测.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 图书统计学 图书统计学
背景情况:
- 精神障碍 (MDs) 代表着全球健康的重大负担,在COVID-19大流行期间显著增加.
- 早期诊断MDs对于改善治疗结果和减少残疾调整寿命年 (DALYs) 至关重要.
- 机器学习 (ML) 和深度学习 (DL) 显示出推动MD诊断的巨大潜力,大量的研究证实了这一点.
研究的目的:
- 在过去十年中对MD诊断的ML和DL研究进行文献分析.
- 审查最近的进展,并确定ML和DL用于心理健康的应用中的关键趋势.
- 绘制研究格局的地图,包括多产的作者,机构和期刊.
主要方法:
- 从Scopus数据库 (2012年1月 - 2023年6月) 中对2811篇期刊文章进行了图书统计分析.
- 对相关和可靠的出版物进行文献搜索和数据过.
- 审查40项选定的研究,以确定当前的研究趋势和流行的算法.
主要成果:
- 在MD诊断的ML中,每年的研究增长率为17.05%.
- "情感障碍杂志"和作者王是主要出版物.
- 中国和美国分别领先于出版物和引用量.
- ML/DL主要集中在抑郁症和精神分裂症,在自闭症,焦虑症,多动症,强迫症和PTSD方面有应用.
- 流行的ML算法包括SVM,决策树和随机森林;DL与神经成像 (MRI,fMRI,EEG) 和双相情感障碍有关.
结论:
- ML和DL是诊断精神障碍的越来越重要的工具,具有显著的研究成果和增长.
- 持续的研究正在探索先进的技术,如DL,LSTM和CNN,以提高诊断准确度.
- 调查结果提供了该领域的全面概述,突出了未来调查和合作的关键领域.
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