Machine Learning in Population and Public Health

TL;DR

This paper introduces how machine learning can be applied to population and public health research to understand social and environmental impacts on health and promote health equity.

Contribution

It highlights opportunities for machine learning to advance public health research and emphasizes the potential for synergy between these fields.

Findings

Identifies key areas where machine learning can impact public health.

Suggests strategies for integrating machine learning into health equity efforts.

Highlights the importance of interdisciplinary collaboration.

Abstract

Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole. We present here a very brief introduction into research in these fields, as well as connections to existing machine learning work to help activate the machine learning community on such topics and highlight specific opportunities where machine learning, public and population health may synergize to better achieve health equity.