ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Optimized dual threshold entity resolution for electronic health record databases--training set size and active learning.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Frequency and Consequences of Cervical Lymph Node Overstaging in Head and Neck Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Blockchain Technology for Healthcare: Facilitating the Transition to Patient-Driven Interoperability.Computational and structural biotechnology journal · 2018Review
- Duplicates, redundancies and inconsistencies in the primary nucleotide databases: a descriptive study.Database : the journal of biological databases and curation · 2017Article
- Article
- Embracing the Sparse, Noisy, and Interrelated Aspects of Patient Demographics for use in Clinical Medical Record Linkage.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
24551372PMC3900213What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.