Deploying Machine Learning Models Using Progressive Web Applications: Implementation Using a Neural Network Prediction Model for Pneumonia Related Child Mortality in The Gambia - Mohammed, Nuredin I.; Jarde, Alexander; Mackenzie, Grant; D'Alessandro, Umberto; Jeffries, David (Frontiers in Public Health, 2022)
Background: Translating research outputs into practical tools for medical practitioners is a neglected area and could have a substantial impact. One of the barriers to implementing artificial intelligence (AI) and machine learning (ML) applications is their practical deployment in the field. Traditional web-based (i.e., server sided) applications are dependent on reliable internet connections, which may not be readily available in rural areas. Native mobile apps require device specific programming skills as well as contemporary hardware and software, with often rapid and unpredictable platform specific changes. This is a major challenge for using AI/ML tools in resource-limited settings. Methods: An emerging technology, progressive web applications (PWAs), first introduced by Google in 2015, offers an opportunity to overcome the challenges of deploying bespoke AI/ML systems. The same PWA code can be implemented across all desktop platforms, iOS and Android phones and tablets. In addition to platform independence, a PWA can be designed to be primarily offline. Results: We demonstrate how a neural network-based pneumonia mortality prediction triage tool was migrated from a typical academic framework (paper and web-based prototype) to a tool that can be used offline on any mobile phone – the most convenient deployment vehicle. After an initial online connection to download the software, the application runs entirely offline, reading data from cached memory, and running code via JavaScript. On mobile devices the application is installed as a native app, without the inconvenience of platform specific code through manufacturer code stores. Discussion: We show that an ML application can be deployed as a platform independent offline PWA using a pneumonia-related child mortality prediction tool as an example. The aim of this tool was to assist clinical staff in triaging children for hospital admission, by predicting their risk of death. PWAs function seamlessly when their host devices lose internet connectivity, making them ideal for e-health apps that can help improve health and save lives in resource-limited settings in line with the UN Sustainable Development Goal 3 (SDG3).