Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States.
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States.
JMIR Ment Health. 2024 Aug 21;11:e53714. doi: 10.2196/53714.
Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.
This review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs.
Using a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs.
A total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms.
The synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.
精神压力及其导致的心理健康障碍（MDs）是一个重大的公共卫生问题。随着机器学习（ML）的出现，有可能利用计算技术来更好地理解和解决精神压力和 MDs。本综述旨在阐明当前在该领域中使用的 ML 方法，为增强精神压力及其随后的 MDs 的检测、预测和分析铺平道路。
本综述旨在调查 ML 方法在精神压力及其随后的 MDs 的检测、预测和分析中的应用范围。
使用严格的范围综述过程，遵循 PRISMA-ScR（系统评价和荟萃分析扩展的首选报告项目，用于范围综述）指南，本研究深入探讨了最新的 ML 算法、预处理技术和在压力和与压力相关的 MDs 背景下使用的数据类型。
共检查了 98 篇同行评议的出版物。研究结果表明，在所有检查的 ML 算法中，支持向量机、神经网络和随机森林模型始终表现出较高的准确性和鲁棒性。生理参数，如心率测量和皮肤反应，由于它们提供了有关压力和与压力相关的 MDs 的丰富解释信息，以及数据采集的相对容易性，因此常被用作压力预测因子。降维技术的应用，包括映射、特征选择、滤波和降噪，经常被视为在训练 ML 算法之前的关键步骤。
本综述的综合分析确定了重大的研究差距，并为该领域确定了未来的方向。这些包括模型可解释性、模型个性化、自然环境的纳入以及实时处理能力，用于检测和预测压力和与压力相关的 MDs。
