This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1340) Electronic Health Records (971) Clinical Informatics (919) Tools, Programs and Algorithms (246)

Published on in Vol 24, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33775, first published .
The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection

The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection

The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection

Authors of this article:

Xiaochun Li1 Author Orcid Image ;   Huiping Xu1 Author Orcid Image ;   Shaun Grannis2 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Xu H, Li X, Zhang Z, Grannis S. Variable selection for latent class analysis in the presence of missing data with application to record linkage. Statistical Methods in Medical Research 2024;33(6):966 View