2_use_case_name: Sequential Coverage Algorithm (SCA) and partial Expectation-Maximization (EM) estimation in Record Linkage
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: CDC's National Center for Health Statistics (NCHS) Data Linkage Program has developed a record linkage program designed to maximize the scientific value of the Center’s population-based surveys. Linked data files enable researchers to examine the factors that influence disability, chronic disease, health care utilization, morbidity, and mortality. The linkage algorithms initially used by the program did not implement machine learning in either deterministic or probabilistic linkages and required several manual steps for these processes. 

Using machine learning in the CDC's National Center for Health Statistics (NCHS) Data Linkage Program offers several benefits. Machine learning algorithms help improve the accuracy and efficiency of data linkage by developing joining methods or blocking groups.  Working with very large datasets can be time-consuming and resource-intensive. By implementing supervised and unsupervised machine learning techniques, the Data Linkage Program can automate the process of developing and selecting blocking groups, reducing manual effort and increasing efficiency. This additionally improves the scalability of data linkages. Machine learning algorithms can continuously learn and adapt based on new data inputs and feedback. This allows the Data Linkage Program to refine their linkage algorithms over time.
12_outputs: CDC's National Center for Health Statistics (NCHS) Data Linkage Program has implemented both supervised and unsupervised machine learning (ML) techniques in their linkage algorithms. The Sequential Coverage Algorithm (SCA), a supervised ML algorithm, is used to develop joining methods (or blocking groups) when working with very large datasets. The unsupervised partial Expectation-Maximization (EM) estimation is used to estimate the proportion of pairs that are matches within each block. Both methods improve linkage accuracy and efficiency.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 2/1/2019
19_date_acq_dev_began: 8/1/2019
20_date_implemented: 8/1/2020
21_date_retired:  
22_dev_method: Developed with contracting resources.
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info: No
28_iqa_compliance:  
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: Data used to evaluate the models include data from the National Hospital Care Survey, the National Health and Nutrition Examination Survey, and the National Health Interview Survey as well as linked administrative data.
34_data_docs: Documentation is widely available
35_demo_features: Sex/Gender
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
39_code_link:  
40_has_ato: Yes
41_system_name: Consolidated Statistical Platform
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.