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 – 总体组成部分 非洲是世界上最年轻的大陆，60%的人口年龄在25岁以下。 早期生命到成年早期是生物、环境和心理社会事件发生的关键窗口 可以显着影响长期的 uzima，这意味着斯瓦希里语的健康/福祉加上最近的情况。 技术进步和在非洲收集的大量数据，出现了前所未有的情况 利用数据科学来确定和改善非洲年轻人的健康轨迹的机会。 重大的分析和计算障碍仍然存在，阻碍了我们利用这些信息来改变的能力 我们提议的研究中心 UZIMA-DS 旨在改变这一现状。 我们将通过数据科学利用健康信息对东非产生有意义的影响。 一个可扩展且可持续的平台，可应用数据同化和先进人工的新方法 基于智能（AI）/机器学习（ML）的方法作为早期预警系统来解决关键问题 在两个领域影响非洲年轻人的健康问题：孕产妇、新生儿和儿童健康和心理 我们的中心满足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进行有意义的使用和分析；2) 利用时间模式 使用基于 AI/ML 的方法进行预测建模来识别轨迹；3) 参与关键； 利益相关者确定将这些模型传播到目标社区并使其可持续发展的途径。 对于我们的母婴健康研究（项目 1），我们将利用大量且多样化的现有数据集 肯尼亚，包括两个人口监测系统、队列研究和医院数据，以开发和 验证基于人工智能/机器学习的预测模型，以识别妊娠不良高风险的育龄妇女 结局（例如妊娠高血压、低出生体重）和晚年非传染性疾病 以及未来面临不良生活后果风险的儿童（例如发育迟缓）。 （项目 2），利用现有的监控数据以及新颖的移动技术（例如移动应用程序、 可穿戴设备）用于开发现有的和新的基于人工智能/机器学习的预测模型来识别青少年和 肯尼亚面临抑郁和自杀意念风险的年轻医护人员将成为我们的中心和项目。 由管理核心、数据管理和分析核心以及传播和可持续性支持 核心，将促进多部门利益相关者的参与，以确定可持续的模型传播 最终，我们的工作将使非洲研究人员能够推进这一目标。 UZIMA-DS 中心通过建设可持续基础设施来满足非洲人持续和不断变化的健康需求， UZIMA-DS 中心可以作为可扩展的模型。 与更大的 DS-I 联盟一起向其他国家和卫生领域转变非洲的护理服务， 确保当代和子孙后代的非洲人能够实现乌兹玛。