Machine Learning in Epidemiology
New handbook chapter provides R code examples and strategies for analyzing complex health data.
Deep Dive
A team of six researchers led by Marvin N. Wright has published "Machine Learning in Epidemiology," a foundational chapter for the Springer Handbook of Epidemiology. The work covers supervised/unsupervised learning, model evaluation, hyperparameter optimization, and interpretable ML. It includes practical R code examples using a heart disease dataset throughout. This provides epidemiologists with methodological tools to analyze increasingly complex and high-dimensional digital health data.
Why It Matters
Equips public health professionals with practical ML skills to tackle modern data challenges in disease tracking and analysis.