Linkability measures to assess the data characteristics for record linkage
Evaluating linkability measures using the isolation forest algorithm to highlight anomalous findings can help identify fitness-for-use issues that must be addressed before initiating the RL process to ensure high-quality linkage outcomes.
Authors
- Lauren R. Lembcke
- Toan C. Ong (ORCID: https://orcid.org/0000-0001-6787-1407)
- Lisa M. Schilling (ORCID: https://orcid.org/0000-0002-6878-189X)
- Shaun J. Grannis (ORCID: https://orcid.org/0000-0002-8093-6639)
- Michael G. Kahn (ORCID: https://orcid.org/0000-0003-4786-6875)
- Andrew C. Hill (ORCID: https://orcid.org/0000-0001-5468-6616)
Institutions
- Indiana University Health (US)
- Regenstrief Institute (US)
- Colorado School of Public Health (US)
- Denver Public Health (US)
- University of Colorado Anschutz Medical Campus (US)
- Indiana University – Purdue University Indianapolis (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1093/jamia/ocae248
- Citations
- 1
- Primary Topic
- Data Quality and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.49
Funders
- Patient-Centered Outcomes Research Institute