Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Wuhan, China.
Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China.
Front Public Health. 2022 Mar 3;10:814366. doi: 10.3389/fpubh.2022.814366. eCollection 2022.
Online mental health service (OMHS) has been named as the best psychological assistance measure during the COVID-19 pandemic. An interpretable, accurate, and early prediction for the demand of OMHS is crucial to local governments and organizations which need to allocate and make the decision in mental health resources. The present study aimed to investigate the influence of the COVID-19 pandemic on the online psychological help-seeking (OPHS) behavior in the OMHS, then propose a machine learning model to predict and interpret the OPHS number in advance. The data was crawled from two Chinese OMHS platforms. Linguistic inquiry and word count (LIWC), neural embedding-based topic modeling, and time series analysis were utilized to build time series feature sets with lagging one, three, seven, and 14 days. Correlation analysis was used to examine the impact of COVID-19 on OPHS behaviors across different OMHS platforms. Machine learning algorithms and Shapley additive explanation (SHAP) were used to build the prediction. The result showed that the massive growth of OPHS behavior during the COVID-19 pandemic was a common phenomenon. The predictive model based on random forest (RF) and feature sets containing temporal features of the OPHS number, mental health topics, LIWC, and COVID-19 cases achieved the best performance. Temporal features of the OPHS number showed the biggest positive and negative predictive power. The topic features had incremental effects on performance of the prediction across different lag days and were more suitable for OPHS prediction compared to the LIWC features. The interpretable model showed that the increase in the OPHS behaviors was impacted by the cumulative confirmed cases and cumulative deaths, while it was not sensitive in the new confirmed cases or new deaths. The present study was the first to predict the demand for OMHS using machine learning during the COVID-19 pandemic. This study suggests an interpretable machine learning method that can facilitate quick, early, and interpretable prediction of the OPHS behavior and to support the operational decision-making; it also demonstrated the power of utilizing the OMHS platforms as an always-on data source to obtain a high-resolution timeline and real-time prediction of the psychological response of the online public.
在线心理健康服务 (OMHS) 已被称为 COVID-19 大流行期间最佳的心理援助措施。对于需要在心理健康资源方面进行分配和决策的地方政府和组织来说，对 OMHS 的需求进行可解释、准确和早期预测至关重要。本研究旨在探讨 COVID-19 大流行对 OMHS 中在线心理求助 (OPHS) 行为的影响，然后提出一种机器学习模型来提前预测和解释 OPHS 数量。数据是从两个中国 OMHS 平台上抓取的。利用语言查询和词汇计数 (LIWC)、基于神经嵌入的主题建模和时间序列分析来构建具有滞后 1、3、7 和 14 天的时间序列特征集。相关性分析用于检查 COVID-19 对不同 OMHS 平台上 OPHS 行为的影响。使用机器学习算法和 Shapley 加法解释 (SHAP) 构建预测模型。结果表明，COVID-19 大流行期间 OPHS 行为的大量增长是一种普遍现象。基于随机森林 (RF) 和包含 OPHS 数量的时间特征、心理健康主题、LIWC 和 COVID-19 病例的特征集的预测模型表现最佳。OPHS 数量的时间特征显示出最大的正预测和负预测能力。主题特征对不同滞后天数的预测性能具有增量效应，与 LIWC 特征相比，更适合 OPHS 预测。可解释模型表明，OPHS 行为的增加受到累积确诊病例和累积死亡人数的影响，而对新确诊病例或新死亡人数的影响不敏感。本研究首次使用机器学习在 COVID-19 大流行期间预测 OMHS 的需求。本研究提出了一种可解释的机器学习方法，可促进 OPHS 行为的快速、早期和可解释预测，并支持运营决策；它还展示了利用 OMHS 平台作为始终在线数据源来获取高分辨率时间线和在线公众心理反应的实时预测的能力。
