Right population, right resources, right algorithm: Using machine learning efficiently and effectively in surgical systems where data are a limited resource.

There is a growing interest in using machine learning algorithms to support surgical care, diagnostics, and public health surveillance in low- and middle-income countries. From our own experience and the literature, we share several lessons for developing such models in settings where the data necessary for algorithm training and implementation is a limited resource. First, the training cohort should be as similar as possible to the population of interest, and recalibration can be used to improve risk estimates when a model is transported to a new context. Second, algorithms should incorporate existing data sources or data that is easily obtainable by frontline health workers or assistants in order to optimize available resources and facilitate integration into clinical practice. Third, the Super Learner ensemble machine learning algorithm can be used to define the optimal model for a given prediction problem while minimizing bias in the algorithm selection process. By considering the right population, right resources, and right algorithm, researchers can train prediction models that are both context-appropriate and resource-conscious. There remain gaps in data availability, affordable computing capacity, and implementation studies that hinder clinical algorithm development and use in low-resource settings, although these barriers are decreasing over time. We advocate for researchers to create open-source code, apps, and training materials to allow new machine learning models to be adapted to different populations and contexts in order to support surgical providers and health care systems in low- and middle-income countries worldwide.