Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage
Description
To improve the accuracy and efficiency of record linkage in CDC’s National Center for Health Statistics (NCHS) Data Linkage Program, particularly for large datasets, by automating the development and selection of blocking groups and reducing manual effort.
Detailed example
Development of joining methods (blocking groups) for large datasets Estimation of the proportion of matched pairs within each block Improved linkage accuracy and efficiency
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Increased accuracy and efficiency in data linkage Automation reduces manual effort and increases scalability Machine learning algorithms adapt and improve over time, refining linkage processes Enables researchers to better examine factors influencing disability, chronic disease, health care utilization, morbidity, and mortality
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Data from the National Hospital Care Survey, the National Health and Nutrition Examination Survey, the National Health Interview Survey, and linked administrative data.