UZIMA-DS: UtiliZing health Information for Meaningful impact in East Africa through Data Science

UZIMA-DS：通过数据科学利用健康信息对东非产生有意义的影响

• 批准号: 10490293
• 负责人: Amina Abubakar Ali
• 金额: $ 129万
• 依托单位: AGA KHAN UNIVERSITY (KENYA)
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490293
• 关键词: Address; Adolescent; Adolescent and Young Adult; Africa; African; Age; Artificial Intelligence; Assimilations; Biological; Caring; Child; Child Health; Cohort Studies; Communication; Communities; Country; Coupled; Data; Data Analyses; Data Analytics; Data Science; Data Set; Data Sources; Depression and Suicide; Development; Developmental Delay Disorders; Ecosystem; Ensure; Environment; Event; FAIR principles; Feeling suicidal; Female of child bearing age; Fostering; Funding; Future; Future Generations; Government; Grant; Guidelines; Health; Health Personnel; Health system; Healthcare; High Risk Woman; Hospitals; Individual; Informatics; Infrastructure; Institutes; Kenya; Lead; Life; Low Birth Weight Infant; Maternal Health; Maternal and Child Health; Medical Research; Mental Depression; Mental Health; Methods; Michigan; Modeling; Moods; Mothers; Outcome; Pathway interactions; Pattern; Personal Satisfaction; Pilot Projects; Policies; Population; Pregnancy Outcome; Private Sector; Reproducibility; Research; Research Institute; Research Personnel; Research Project Grants; Research Support; Research Training; Resource-limited setting; Resources; Risk; System; Training Programs; Trust; United States National Institutes of Health; Universities; Woman; Work; Youth; antenatal; cardiovascular disorder risk; care delivery; career; clinically relevant; commercialization; computational platform; computer science; data ecosystem; data hub; data interoperability; data management; data sharing; early childhood; high risk; improved; mHealth; machine learning method; machine learning prediction; mobile application; mobile computing; multidisciplinary; multimodal data; neonatal health; novel; novel strategies; open data; population health; predictive modeling; pregnancy hypertension; programs; psychosocial; risk prediction model; statistics; surveillance data; synergism; translational pipeline; university student; young adult

PROJECT SUMMARY – Overall Component Africa is the youngest continent in the world, with 60% of its population under the age of 25. The span between early life to young adulthood represents a critical window where biological, environment and psychosocial events can significantly impact long- term uzima, which means health/well-being in Swahili. Coupled with the recent technological advances and the enormous volumes of data collected in Africa, there is an unprecedented opportunity to leverage data science to identify and improve the health trajectories of young Africans. However, significant analytical and computational barriers persist that impede our ability to use this information to change care at the community and individual level. Our proposed Research Hub, UZIMA-DS, aims to change this narrative by UtiliZing health Information for Meaningful impact in East Africa through Data Science. We will create a scalable and sustainable platform to apply novel approaches to data assimilation and advanced artificial intelligence (AI)/machine learning (ML)-based methods to serve as early warning systems to address critical health issues impacting young Africans in two domains: maternal, newborn and child health and mental health. Our Hub addresses three critical needs across the translational spectrum of data science: 1) Harmonization of multimodal data sources for meaningful use and analyses; 2) Leveraging temporal patterns of data to identify trajectories through prediction modeling using AI/ML-based methods; and 3) Engaging with key stakeholders to identify pathways for dissemination and sustainability of these models into target communities. For our Maternal and Child Health Study (Project 1), we will leverage the large and diverse existing data sets in Kenya, including two demographic surveillance systems, cohort studies and hospital data, to develop and validate AI/ML-based prediction models to identify women of childbearing age at high risk for poor pregnancy outcomes (e.g., pregnancy-induced hypertension, low birthweight) and non-communicable diseases later in life and children at risk of future poor life outcomes (e.g., developmental delays). For our Mental Health Study (Project 2), leverage existing surveillance data as well as novel mobile technologies (e.g., mobile apps, wearables) for the development of existing and new AI/ML-based prediction models to identify adolescents and young healthcare workers at risk of depression and suicide ideation in Kenya. Our Hub and Projects will be supported by an Admin Core, Data Management and Analysis Core, and a Dissemination and Sustainability Core, which will facilitate engagement with multisectoral stakeholders to identify sustainable model dissemination pathways into target communities. Ultimately, our work will empower African researchers to carry forward the UZIMA-DS Hub to address on-going and evolving health needs of Africans by building sustainable infrastructure, expertise, and partnerships for long-lasting impact. The UZIMA-DS Hub can serve as a model that can be scaled to other countries and health domains with the greater DS-I consortium to transform care delivery in Africa, ensuring that current and future generations of Africans can achieve uzima.

项目总结-整体组成部分