Evidence map›Paper›PMID 19567789›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

An empiric modification to the probabilistic record linkage algorithm using frequency-based weight scaling.

Vivienne J Zhu, Marc J Overhage, James Egg, Stephen M Downs, Shaun J Grannis

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

  1. Article
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  5. Evaluating the effect of data standardization and validation on patient matching accuracy.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  6. Machine Learning Approaches to Identify Nicknames from A Statewide Health Information Exchange.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019
    Article
  7. Population-Based Linkage of Big Data in Dental Research.International journal of environmental research and public health · 2018
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Vivienne J ZhuRegenstrief Institute, Inc., 410 West 10 Street, Suite 2000, Indianapolis, IN 46202-3012, USA. sgrannis@regenstrief.org
Marc J Overhage
James Egg
Stephen M Downs
Shaun J Grannis

Funding

REGENSTRIEF MEDICAL INFORMATICS RESEARCH FELLOWSHIPST15LM007117 · NLM · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI FINNELL, JOHN T · 1997 to 2011
$5.0M
NLM NIH HHS 5T15LM007117NLM NIH HHS T15 LM007117PHS HHS U22MC06969
6 · The paper itself

Abstract

objectiveTo incorporate value-based weight scaling into the Fellegi-Sunter (F-S) maximum likelihood linkage algorithm and evaluate the performance of the modified algorithm. Background Because healthcare data are fragmented across many healthcare systems, record linkage is a key component of fully functional health information exchanges. Probabilistic linkage methods produce more accurate, dynamic, and robust matching results than rule-based approaches, particularly when matching patient records that lack unique identifiers. Theoretically, the relative frequency of specific data elements can enhance the F-S method, including minimizing the false-positive or false-negative matches. However, to our knowledge, no frequency-based weight scaling modification to the F-S method has been implemented and specifically evaluated using real-world clinical data.

methodsThe authors implemented a value-based weight scaling modification using an information theoretical model, and formally evaluated the effectiveness of this modification by linking 51,361 records from Indiana statewide newborn screening data to 80,089 HL7 registration messages from the Indiana Network for Patient Care, an operational health information exchange. In addition to applying the weight scaling modification to all fields, we examined the effect of selectively scaling common or uncommon field-specific values.

resultsThe sensitivity, specificity, and positive predictive value for applying weight scaling to all field-specific values were 95.4, 98.8, and 99.9%, respectively. Compared with nonweight scaling, the modified F-S algorithm demonstrated a 10% increase in specificity with a 3% decrease in sensitivity.

conclusionBy eliminating false-positive matches, the value-based weight modification can enhance the specificity of the F-S method with minimal decrease in sensitivity.

Indexed as

AlgorithmsCommunity NetworksMedical Record LinkageHumansIndianaInfant, NewbornLikelihood FunctionsNeonatal ScreeningRegistriesSensitivity and Specificity

Identifiers

PMID19567789
PMCPMC2744724

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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.