Finnegan Amy, Potenziani David D, Karutu Caroline, Wanyana Irene, Matsiko Nicholas, Elahi Cyrus, Mijumbi Nobert, Stanley Richard, Vota Wayan
IntraHealth International, Chapel Hill, NC, United States.
Duke Global Health Institute, Durham, NC, United States.
Front Big Data. 2022 Jul 27;5:553673. doi: 10.3389/fdata.2022.553673. eCollection 2022.
The rapid emergence of machine learning in the form of large-scale computational statistics and accumulation of data offers global health implementing partners an opportunity to adopt, adapt, and apply these techniques and technologies to low- and middle-income country (LMIC) contexts where we work. These benefits reside just out of the reach of many implementing partners because they lack the experience and specific skills to use them. Yet the growth of available analytical systems and exponential growth of data require the global digital health community to become conversant in this technology to continue to make contributions to help fulfill our missions. In this community case study, we describe the approach we took at IntraHealth International to inform the use case for machine learning in global health and development. We found that the data needed to take advantage of machine learning were plentiful and that an international, interdisciplinary team can be formed to collect, clean, and analyze the data at hand using cloud-based (e.g., Dropbox, Google Drive) and open source tools (e.g., R). We organized our work as a "sprint" lasting roughly 10 weeks in length so that we could rapidly prototype these approaches in order to achieve institutional buy in. Our initial sprint resulted in two requests in subsequent workplans for analytics using the data we compiled and directly impacted program implementation.
机器学习以大规模计算统计的形式迅速兴起以及数据的积累，为全球卫生实施伙伴提供了一个机会，使其能够在我们开展工作的低收入和中等收入国家（LMIC）环境中采用、调整和应用这些技术。然而，许多实施伙伴无法获得这些好处，因为他们缺乏使用这些技术的经验和特定技能。然而，可用分析系统的增长和数据的指数级增长要求全球数字卫生界熟悉这项技术，以便继续做出贡献，帮助实现我们的使命。在这个社区案例研究中，我们描述了国际健康组织（IntraHealth International）为阐明机器学习在全球卫生与发展中的用例所采取的方法。我们发现，利用机器学习所需的数据非常丰富，可以组建一个国际跨学科团队，使用基于云的工具（如Dropbox、谷歌云端硬盘）和开源工具（如R）来收集、清理和分析手头的数据。我们将工作组织成一个为期约10周的“冲刺”，以便能够快速将这些方法制作成原型，从而获得机构的认可。我们最初的冲刺在随后的工作计划中产生了两项使用我们汇编的数据进行分析的请求，并直接影响了项目实施。
