PDF(Pubmed)
Abstract：
OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.
METHODS: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya.
UNASSIGNED: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches.
CONCLUSIONS: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.
METHODS: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya.
UNASSIGNED: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient healthcare workforce to develop sustainable economic growth in Kenya, East Africa, and ultimately neighboring countries in sub-Saharan Africa. This protocol paper provides an opportunity to share the planned study implementation methods and approaches.
CONCLUSIONS: A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance.
摘要：
目的：这项研究旨在识别和验证加权传感器流特征，以预测肯尼亚医护人员中严重抑郁发作和未来情绪的近期风险。
方法：该研究将部署一个移动应用程序（app）平台，并使用新的数据科学分析方法（人工智能和机器学习）来识别来自内罗毕五家医疗机构的500名随机抽样的医护人员中的精神健康障碍预测因子。肯尼亚。
■这项研究将为创建用于快速诊断的敏捷和可扩展系统奠定基础，该系统可以为减轻抑郁症的精确干预措施提供指导，并确保健康，有弹性的医疗劳动力，以发展肯尼亚的可持续经济增长，东非,最终是撒哈拉以南非洲的邻国。本协议文件提供了一个机会，分享计划的研究实施方法和方法。
结论：一个可扩展并可用于了解和改善心理健康结果的移动技术平台至关重要。
方法：该研究将部署一个移动应用程序（app）平台，并使用新的数据科学分析方法（人工智能和机器学习）来识别来自内罗毕五家医疗机构的500名随机抽样的医护人员中的精神健康障碍预测因子。肯尼亚。
■这项研究将为创建用于快速诊断的敏捷和可扩展系统奠定基础，该系统可以为减轻抑郁症的精确干预措施提供指导，并确保健康，有弹性的医疗劳动力，以发展肯尼亚的可持续经济增长，东非,最终是撒哈拉以南非洲的邻国。本协议文件提供了一个机会，分享计划的研究实施方法和方法。
结论：一个可扩展并可用于了解和改善心理健康结果的移动技术平台至关重要。
