More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization.
One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can’t be used.
The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers. The company says the tool, named the Connected Health AI Network (CHAIN), was able to reduce vaccine wastage by 96 percent across three regions of Tanzania. Now it is working to scale that success across Tanzania and Mozambique.
Machine learning is not just focused on algorithms; it is also concerned with how machines can learn from humans. Human-in-the-loop (HIL) machine learning can markedly decrease the number of labelled data points necessary for prediction tasks, instead relying on domain expertise. HIL ML focuses on how to use human insight: to efficiently train machine learning models and access difficult-to-acquire information that can increase the accuracy and precision of predictive models.
Machine learning is rarely put to work to tackle problems in global health. The reason is the lack of large-scale, high-quality labelled data. Accurate immunization data requires engagement from frontline health workers and trust that data will empower and not merely check a box.
macro-eyes will deploy human-in-the-loop machine learning to engage frontline caregivers and gather valuable information to power the predictive supply chain for vaccines. Frontline health workers are domain experts. They know more than anyone about the delivery of vaccines where they work and how the catchment population perceives immunization and will access facilities.
By meaningfully engaging health workers and encoding the provided information for machine learning, routine data entry will be reduced and data quality and actionable insight increased. Direct engagement also creates data champions at the point of care who feel valued. Health worker insight on populations and demand, conveyed via WhatsApp text message will programmatically augment the analysis of supply chain and immunization data.
This initiative will introduce health workers to the link between data quality and insight, opening the door for widespread use of machine learning for global health. This will be one of the first deployments of machine learning – specifically human-in-the-loop machine learning – for global health.
News Source: MIT
