通过收获社交媒体来检测精神疾病:综合文献综述
Shahid Munir Shah1, Mahmoud Mohammad Aljawarneh2, Muhammad Aamer Saleem1
1Faculty of Engineering Sciences and Technology, Hamdard University, Karachi, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
本研究审查了机器学习和深度学习模型,用于从社交媒体数据中检测精神疾病,特别是抑郁症. 它提供了构建此类系统的路线图,分析了100多个模型.
科学领域:
- 计算精神病学是一种计算精神病学.
- 数字心理健康数字心理健康
- 社交媒体分析.
背景情况:
- 精神疾病诊断传统上依赖于容易出错的手动方法.
- 社交媒体为识别心理健康状况提供了丰富的数据来源.
- 现有的审查缺乏基于社交媒体的精神疾病检测系统的全面路线图.
研究的目的:
- 通过使用社交媒体数据,对识别精神疾病,特别是抑郁症进行彻底分析.
- 审查最近在机器学习 (ML) 和深度学习 (DL) 中的精神疾病检测方面的进展.
- 为设计使用社交媒体和ML/DL的精神疾病检测系统提供全面的路线图.
主要方法:
- 对100多种ML,DL和自然语言处理 (NLP) 模型进行了全面的文献综述.
- 对社交媒体数据的数据采集,预处理和特征提取技术的分析.
- 对审查模型的优势和技术贡献进行批判性分析.
主要成果:
- 从社交媒体中识别了许多ML,DL和NLP模型用于心理疾病检测.
- 详细介绍了数据收集,预处理和功能工程的过程.
- 强调了各种方法的优点和局限性.
结论:
- 用ML/DL分析的社交媒体数据显示,精确的精神疾病检测有前途.
- 本综述为数字心理健康领域的研究人员和开发人员提供了详细的路线图.
- 进一步的研究可以利用这份路线图,建立更有效的精神疾病检测系统.
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