Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2020-08 (month precision)Source: Aug-20

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Health and Medical

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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

System outputs

Development of joining methods (blocking groups) for large datasets Estimation of the proportion of matched pairs within each block Improved linkage accuracy and efficiency

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

Consolidated Statistical Platform

Custom-developed code

Yes

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

Yes

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

b) Sex

Training and evaluation data

Data from the National Hospital Care Survey, the National Health and Nutrition Examination Survey, the National Health Interview Survey, and linked administrative data.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported