NHANES-GCP: Leveraging the Google Cloud Platform and BigQuery ML for
  reproducible machine learning with data from the National Health and
  Nutrition Examination Survey
B. Ross Katz Abdul Khan James York-Winegar Alexander J. Titus
Summary: NHANES, the National Health and Nutrition Examination Survey, is a
program of studies led by the Centers for Disease Control and Prevention (CDC)
designed to assess the health and nutritional status of adults and children in
the United States (U.S.). NHANES data is frequently used by biostatisticians
and clinical scientists to study health trends across the U.S., but every
analysis requires extensive data management and cleaning before use and this
repetitive data engineering collectively costs valuable research time and
decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a
Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and
Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that
automates the data engineering and management aspects of working with NHANES
data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less
than $15/yr of ongoing costs for hosting the NHANES data, all while providing
researchers with clean data tables that can readily be integrated for
large-scale analyses. We provide examples of leveraging BigQuery ML to carry
out the process of selecting data, integrating data, training machine learning
and statistical models, and generating results all from a single SQL-like
query. NHANES-GCP is designed to enhance the reproducibility of analyses and
create a well-engineered NHANES data resource for statistics, machine learning,
and fine-tuning Large Language Models (LLMs).
  Availability and implementation" NHANES-GCP is available at
https://github.com/In-Vivo-Group/NHANES-GCP