| Autor: |
Willie Njoroge, Rachel Maina, Elena Frank, Lukoye Atwoli, Zhenke Wu, Anthony K Ngugi, Srijan Sen, JianLi Wang, Stephen Wong, Jessica A Baker, Eileen M Weinheimer-Haus, Linda Khakali, Andrew Aballa, James Orwa, Moses K Nyongesa, Jasmit Shah, Akbar K Waljee, Amina Abubakar, Zul Merali |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
BMC Research Notes, Vol 16, Iss 1, Pp 1-9 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1756-0500 |
| DOI: |
10.1186/s13104-023-06498-6 |
| Popis: |
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. Approach 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. Expectation 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. Conclusion A mobile technology platform that is scalable and can be used to understand and improve mental health outcomes is of critical importance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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