The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.
Journal of Systems and Software , volume = 231, pages = 112612, year = 2026, month = jan, doi =
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Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.