title: Optimized dual threshold entity resolution for electronic health record databases--training set size and active learning.
abstract: Clinical databases may contain several records for a single patient. Multiple general entity-resolution algorithms have been developed to identify such duplicate records. To achieve optimal accuracy, algorithm parameters must be tuned to a particular dataset. The purpose of this study was to determine the required training set size for probabilistic, deterministic and Fuzzy Inference Engine (FIE) algorithms with parameters optimized using the particle swarm approach. Each algorithm classified potential duplicates into: definite match, non-match and indeterminate (i.e., requires manual review). Training sets size ranged from 2,000-10,000 randomly selected record-pairs. We also evaluated marginal uncertainty sampling for active learning. Optimization reduced manual review size (Deterministic 11.6% vs. 2.5%; FIE 49.6% vs. 1.9%; and Probabilistic 10.5% vs. 3.5%). FIE classified 98.1% of the records correctly (precision=1.0). Best performance required training on all 10,000 randomly-selected record-pairs. Active learning achieved comparable results with 3,000 records. Automated optimization is effective and targeted sampling can reduce the required training set size.
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
authors.forename: Erel
authors.initials: E
authors.lastname: Joffe
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Byrne
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
authors.forename: Phillip
authors.initials: P
authors.lastname: Reeder
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX. ; Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX.
authors.forename: Jorge R
authors.initials: JR
authors.lastname: Herskovic
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
authors.forename: Craig W
authors.initials: CW
authors.lastname: Johnson
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX. ; UT Houston - Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX.
authors.forename: Allison B
authors.initials: AB
authors.lastname: McCoy
authors.affiliation: School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX. ; Division of General Internal Medicine, Department of Internal Medicine, Medical School, The University of Texas Health Science Center at Houston, Houston, TX.
authors.forename: Elmer V
authors.initials: EV
authors.lastname: Bernstam
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057286:Electronic Health Records; D017143:Fuzzy Logic