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.

项目摘要 - 整体组成部分 非洲是世界上最年轻的大陆，其人口60％以下25岁。 年轻成年的早期生活代表着一个关键窗口，生物，环境和社会心理事件 可以显着影响长期的乌齐玛，这意味着斯瓦希里语的健康/福祉。加上最近的 技术进步和在非洲收集的大量数据，存在前所未有的 利用数据科学来识别和改善非洲人的健康轨迹的机会。然而， 大量的分析和计算障碍持续存在，阻碍了我们使用此信息更改的能力 在社区和个人层面上照顾。我们拟议的研究中心Uzima-DS旨在改变这一点 通过利用健康信息来通过数据科学对东非的有意义影响进行叙述。我们将创建 可扩展且可持续的平台，将新颖的方法应用于数据同化和高级人工 智能（AI）/机器学习（ML）的方法作为预警系统，以解决关键 影响两个领域的年轻非洲人的健康问题：物物，新生儿和儿童健康以及精神 健康。我们的枢纽解决了数据科学转化范围的三个关键需求：1） 统一多模式数据源，以进行有意义的使用和分析； 2）利用临时模式 数据通过使用基于AI/ML的方法进行预测建模来识别轨迹； 3）与钥匙互动 利益相关者将这些模型传播和可持续性的途径识别为目标社区。 对于我们的母亲和儿童健康研究（项目1），我们将利用大型和潜水员的现有数据集 肯尼亚，包括两个人口监视系统，队列研究和医院数据，以开发和 验证基于AI/ML的预测模型，以确定怀孕不良风险的育龄妇女 结局（例如怀孕引起的高血压，低出生体重）和生命之后的非交通疾病 以及有未来差的生活结果的孩子（例如发展延迟）。对于我们的心理健康研究 （项目2），利用现有的监视数据以及新颖的移动技术（例如，移动应用程序， 可穿戴设备）用于开发现有和新的基于AI/ML的预测模型，以识别青少年和 肯尼亚有抑郁症和自杀想法风险的年轻医疗保健工作者。我们的枢纽和项目将是 由管理员核心，数据管理和分析核心以及传播和可持续性的支持 核心，这将促进与多部门利益相关者的参与，以确定可持续的模型传播 进入目标社区的途径。最终，我们的工作将使非洲研究人员有能力继续 Uzima-DS枢纽通过建立可持续基础设施来满足非洲人的持续和不断发展的健康需求， 专业知识以及持久影响的合作伙伴关系。 Uzima-DS集线器可以用作可以缩放的模型 到其他国家和拥有更大DS-I联盟的其他国家和卫生领域，以改变非洲的护理服务， 确保当前和后代的非洲人可以实现Uzima。