青少年心理健康预测模型

Sakirulai O. Isiaq, Lawrence Dawson
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引用次数: 0

摘要

心理健康是个人认识到自己的能力并能有效地应付生活压力的一种幸福状态。不幸的是,围绕年轻人心理健康的问题遍及世界上每一个社会经济群体。这包括缺乏获得适当医疗服务的机会以及与之相关的污名等因素。近年来,各种研究表明,计算机应用越来越多地有助于管理人类福祉和其他生活活动。随后,机器学习模型在预测未来活动和事件方面被证明是有效的。这项工作涉及三个模型的发展,其目的是建立一个基准的精神健康障碍的预测。记录的结果很有希望,AUC得分为96%(焦虑)和93%(抑郁)。这项工作为机器学习模型的部署提供了基础,用于开发计算机应用程序,可以改善对常见精神健康障碍(即焦虑和抑郁)的预测,因此,它可以从受控环境升级到现实世界的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mental health predictive models for triaging young adults
Mental health is a state of well-being in which an individual realises own abilities and can productively cope with the stresses of life. $U$ nfortunately, issues surrounding the mental health of young adults span every socio-economic group in the world. Such include a lack of access to adequate medical service and associated stigma among other factors. In recent times, various studies have indicated computer applications are increasingly contributing to the management of human well-being and other life activities. Subsequently, machine learning models have proved effective in predicting future activities and occurrences. This work involves the development of three models, which aim to establish a benchmark for mental health disorders prediction. The recorded results are promising with AUC scores of 96% (anxiety) and 93% (depression). This work provides the groundwork around the deployment of machine learning models for the development of computer applications that can improve the prediction of common mental health disorders, namely anxiety and depression, hence, it could be upscaled from a controlled environment to real-world application.
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