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-整体组件 非洲是世界上最年轻的大陆，60%的人口年龄在25岁以下。之间的跨度 从生命早期到青年期是一个关键的窗口， 可以显著影响长期的uzima，这意味着斯瓦希里语的健康/福祉。加上近期 技术进步和在非洲收集的大量数据， 利用数据科学来识别和改善非洲年轻人的健康轨迹。然而，在这方面， 重要的分析和计算障碍仍然存在，阻碍了我们利用这些信息进行改变的能力 在社区和个人层面上的关怀。我们提议的研究中心UZIMA-DS旨在改变这一点 通过数据科学利用健康信息对东非产生有意义的影响。我们将创建 一个可扩展和可持续的平台，应用新的方法进行数据同化和先进的人工智能。 智能（AI）/机器学习（ML）的方法，作为早期预警系统，以解决关键的 在两个领域影响非洲青年的健康问题：孕产妇、新生儿和儿童健康以及心理健康 健康我们的中心解决了数据科学转化领域的三个关键需求：1） 协调多模式数据源，以便进行有意义的使用和分析; 2）利用 通过使用基于AI/ML的方法进行预测建模来识别轨迹的数据;以及3）与关键 利益攸关方确定向目标社区传播这些模式并使其可持续的途径。 对于我们的母婴健康研究（项目1），我们将利用现有的大型和多样化的数据集， 肯尼亚，包括两个人口监测系统、队列研究和医院数据，以发展和 验证基于AI/ML的预测模型，以识别处于不良妊娠高风险的育龄妇女 结果（例如，妊娠高血压、低出生体重）和非传染性疾病 以及面临未来不良生活后果风险的儿童（例如，发育迟缓）。我们的心理健康研究 （项目2），利用现有的监测数据以及新颖的移动的技术（例如，移动的应用程序， 可穿戴设备）用于开发现有的和新的基于AI/ML的预测模型，以识别青少年， 在肯尼亚，年轻的医护人员面临抑郁和自杀意念的风险。我们的中心和项目将是 由一个管理核心、数据管理和分析核心以及一个传播和可持续发展中心提供支持。 核心，这将促进与多部门利益相关者的接触，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能够发扬 UZIMA-DS Hub通过建设可持续的基础设施，满足非洲人持续和不断变化的健康需求， 专业知识和伙伴关系，以产生持久的影响。UZIMA-DS Hub可以作为可扩展的模型 与更大的DS-I联盟合作，向其他国家和卫生领域提供服务，以改变非洲的医疗服务， 确保当代和后代非洲人能够实现uzima。