Improving AI/ML-readiness of Synthetic Data in a Resource-Constrained Setting

在资源受限的环境中提高合成数据的 AI/ML 准备度

• 批准号: 10841728
• 负责人: Amina Abubakar Ali
• 金额: $ 25.44万
• 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/10841728
• 关键词: Address; Administrative Supplement; Africa; Artificial Intelligence; Big Data; Cellular Phone; Child Health; Cloud Computing; Computational Technique; Country; Data; Data Science; Data Set; Data Sources; Development; Disease; Disparity population; Documentation; Environment; Event; FAIR principles; Funding; Goals; Graph; Health; Health Policy; Health Sciences; Heterogeneity; Individual; Investments; Kenya; Knowledge; Laws; Machine Learning; Maternal Health; Mental Health; Methods; Michigan; Modeling; Nature; Outcome; Penetration; Politics; Population; Privacy; Readiness; Research; Research Personnel; Resource-limited setting; Resources; Rural; Statistical Distributions; System; Techniques; Testing; Training; United States National Institutes of Health; Universities; Validation; Work; career; data management; data preservation; data sharing; improved; innovation; machine learning method; machine learning model; marginalized population; multimodal data; neonatal health; novel strategies; parent project; population based; preservation; response; social; social determinants; student training

PROJECT SUMMARY / ABSTRACT The parent project, UZIMA-DS (UtiliZing Health Information for Meaningful Impact in East Africa through Data Science), aims to create a scalable and sustainable platform to apply novel approaches to data assimilation and advanced artificial intelligence (AI)/machine learning (ML)-based methods to improve health outcomes in two health domains: maternal, newborn and child health; and mental health. Led by the Aga Khan University in East Africa (AKU) and the University of Michigan, UZIMA-DS is a U54 Research Hub funded under the NIH Data Sci- ence for Health Discovery and Innovation in Africa Initiative. During these first two years, UZIMA-DS has focused on acquiring and harmonizing multimodal data sources. However, we and many other DS-I Africa awardees have encountered several barriers to efficiently and effectively creating AI-ready data sets, which include: 1) regulatory concerns around privacy and confidentiality, 2) heterogeneity in data laws across countries limiting the accessibil- ity of data, and 3) a lack of sufficient datasets not only for training ML models and validation but also for training students and early career investigators for capacity building. Synthetic data, or data that is generated artificially using computational techniques such as AI, is a promising technique that could address these barriers and ena- ble the broad sharing of AI-ready data sets. As part of this administrative supplement, we propose to create an AI-ready synthetic data set using one of our real UZIMA-DS data sets from Kenya: the Kaloleni-Rabai Health and Demographic Surveillance Systems (KRHDSS). KRHDSS is a population-based demographic and health surveil- lance system established in 2017 by AKU. Information is collected at least annually on ~40 demographic, health, social determinants of disease, and vital events from a resident population of about 99,000 individuals. Leverag- ing our preliminary work using a Microsoft Azure instance, we will create AI-ready synthetic datasets for research and training and evaluate whether causal relationships in real data are preserved in synthetic datasets. The overarching goal of this proposal is to “put data to work” by developing a roadmap for the curation and use of AI-ready synthetic data using FAIR principles (findable, accessible, interoperable, and re‑usable) that can be eas- ily accessed and shared for research and training purposes across the globe. Ultimately, this work has the poten- tial to promote more effective and efficient sharing of AI-ready data globally. Using cloud infrastructure and Health and Demographic Surveillance Systems data from rural Kenya as a use case, this work has immediate implications for how AI-ready data can be leveraged in resource-constrained settings to improve data driven health policy decisions for traditionally disadvantaged and marginalized groups.

项目总结/摘要 母项目UZIMA-DS（通过数据利用卫生信息对东非产生有意义的影响） 科学），旨在创建一个可扩展和可持续的平台，以应用新的方法进行数据同化， 基于先进人工智能（AI）/机器学习（ML）的方法，以改善两个国家的健康状况 卫生领域：孕产妇、新生儿和儿童健康;以及精神健康。由东印度阿加汗大学牵头， 非洲（AKU）和密歇根大学UZIMA-DS是一个U 54研究中心，由NIH数据科学资助， 非洲健康发现和创新倡议。在最初的两年里，UZIMA-DS专注于 获取和协调多模式数据源。然而，我们和许多其他DS-I非洲获奖者 在高效和有效地创建AI就绪数据集方面遇到了几个障碍，其中包括：1）监管 对隐私和保密性的担忧，2）各国数据法的异质性限制了数据的可访问性， 数据量，以及3）缺乏足够的数据集，不仅用于训练ML模型和验证，而且用于训练 学生和早期职业调查人员的能力建设。合成数据，或人工生成的数据 使用人工智能等计算技术，是一种很有前途的技术，可以解决这些障碍， 实现AI就绪数据集的广泛共享。作为这项行政补充的一部分，我们建议设立一个 人工智能就绪的合成数据集，使用我们来自肯尼亚的真实的UZIMA-DS数据集之一：Kaloleni-Rabai健康和 人口监测系统。KRHDSS是一项基于人口的人口和健康调查- AKU于2017年建立了Lance系统。每年至少收集约40项人口统计、健康、 疾病的社会决定因素，以及来自约99，000人的常住人口的生命事件。杠杆- 通过使用Microsoft Azure实例进行初步工作，我们将创建AI就绪的合成数据集用于研究 以及训练和评估真实的数据中的因果关系是否保留在合成数据集中。的 该提案的总体目标是通过制定管理和使用 使用FAIR原则（可查找、可访问、可互操作和可重用）的AI就绪合成数据， 在地球仪上为研究和培训目的而访问和共享。最终，这项工作具有潜力- 这将有助于在全球范围内更有效地共享AI就绪数据。使用云基础架构和 健康和人口监测系统的数据从农村肯尼亚作为一个用例，这项工作有立即 对如何在资源受限的环境中利用AI就绪数据以改善数据驱动的影响 为传统上处于不利地位和被边缘化的群体作出卫生政策决定。

项目成果

Obesity and Risk of Hypertension in Preadolescent Urban School Children: Insights from a Developing Country.

青春期前城市学童的肥胖和高血压风险：来自发展中国家的见解。

• DOI: 10.21203/rs.3.rs-4213965/v1
• 发表时间: 2024
• 期刊: Research square
• 作者: Akhtar,Samina;Khan,Shahid;Aziz,Namra;Magsi,MuhammadImran;Samad,Zainab;Iqbal,Romaina;Almas,Aysha
• 通讯作者: Almas,Aysha

COVID-19 vaccination refusal trends in Kenya over 2021.

• DOI: 10.1016/j.vaccine.2022.12.066
• 发表时间: 2023-01-27
• 期刊: VACCINE
• 影响因子: 5.5
• 作者: Rego, Ryan T.;Kenney, Brooke;Ngugi, Anthony K.;Espira, Leon;Orwa, James;Siwo, Geoffrey H.;Sefa, Christabel;Shah, Jasmit;Weinheimer-Haus, Eileen;Delius, Antonia Johanna Sophie;Pape, Utz Johann;Irfan, Furqan B.;Abubakar, Amina;Shah, Reena;Wagner, Abram;Kolars, Joseph;Boulton, Matthew L.;Hofer, Timothy;Waljee, Akbar K.
• 通讯作者: Waljee, Akbar K.

Mental health and psychological well-being of Kenyan adolescents from Nairobi and the Coast regions in the context of COVID-19.

• DOI: 10.1186/s13034-023-00613-y
• 发表时间: 2023-05-19
• 期刊: CHILD AND ADOLESCENT PSYCHIATRY AND MENTAL HEALTH
• 影响因子: 5.6
• 作者: Mbithi, Gideon;Mabrouk, Adam;Sarki, Ahmed;Odhiambo, Rachel;Namuguzi, Mary;Dzombo, Judith Tumaini;Atukwatse, Joseph;Kabue, Margaret;Mwangi, Paul;Abubakar, Amina
• 通讯作者: Abubakar, Amina

Comparison of logistic regression with regularized machine learning methods for the prediction of tuberculosis disease in people living with HIV: cross-sectional hospital-based study in Kisumu County, Kenya.

逻辑回归与正则化机器学习方法预测艾滋病毒感染者结核病的比较：肯尼亚基苏木县医院的横断面研究。

• DOI: 10.21203/rs.3.rs-3354948/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Orwa,James;Oduor,Patience;Okelloh,Douglas;Gethi,Dickson;Agaya,Janet;Okumu,Albert;Wandiga,Steve
• 通讯作者: Wandiga,Steve

Psychometric evaluation of PHQ-9 and GAD-7 among community health volunteers and nurses/midwives in Kenya following a nation-wide telephonic survey.

• DOI: 10.3389/fpsyt.2023.1123839
• 发表时间: 2023
• 期刊: FRONTIERS IN PSYCHIATRY
• 影响因子: 4.7
• 作者: Odero, Sabina Adhiambo;Mwangi, Paul;Odhiambo, Rachel;Nzioka, Brenda Mumbua;Shumba, Constance;Ndirangu-Mugo, Eunice;Abubakar, Amina
• 通讯作者: Abubakar, Amina