Evidence map›Paper›PMID 30414475›Full record

ArticleJournal of biomedical informatics2018

A method for harmonization of clinical abbreviation and acronym sense inventories.

Lisa V Grossman, Elliot G Mitchell, George Hripcsak, Chunhua Weng, David K Vawdrey

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. "No BLT": The Rise and Risk of Acronyms in the Medical Record.HEC forum : an interdisciplinary journal on hospitals' ethical and legal issues · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Sequence Labeling for Disambiguating Medical Abbreviations.Journal of healthcare informatics research · 2023
    Article
  6. Article
  7. Article
  8. Article
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.

Lisa V GrossmanDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; College of Physicians and Surgeons, Columbia University, New York, NY, USA. Electronic address: lvg2104@cumc.columbia.edu.
Elliot G MitchellDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Chunhua WengDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
David K VawdreyDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Value Institute, NewYork-Presbyterian Hospital, New York, NY, USA.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical DataR01LM009886 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI WENG, CHUNHUA · 2009 to 2020
$5.3M
Addressing Hospital Patient Information Needs Using a PHR PortalR01HS021816 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI VAWDREY, DAVID K. · 2013 to 2016
$2.0M
Automated Knowledge Engineering Methods to Improve Consumers' Comprehension of their Health RecordsF31LM013054 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GROSSMAN LIU, LISA · 2019 to 2020
$97k
AHRQ HHS R01 HS021816NLM NIH HHS F31 LM013054NLM NIH HHS R01 LM006910NLM NIH HHS R01 LM009886NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

backgroundPrevious research has developed methods to construct acronym sense inventories from a single institutional corpus. Although beneficial, a sense inventory constructed from a single institutional corpus is not generalizable, because acronyms from different geographic regions and medical specialties vary greatly.

objectiveDevelop an automated method to harmonize sense inventories from different regions and specialties towards the development of a comprehensive inventory.

methodsThe method involves integrating multiple source sense inventories into one centralized inventory and cross-mapping redundant entries to establish synonymy. To evaluate our method, we integrated 8 well-known source inventories into one comprehensive inventory (or metathesaurus). For both the metathesaurus and its sources, we evaluated the coverage of acronyms and their senses on a corpus of 1 million clinical notes. The corpus came from a different institution, region, and specialty than the source inventories.

resultsIn the evaluation using clinical notes, the metathesaurus demonstrated an acronym (short form) micro-coverage of 94.3%, representing a substantial increase over the two next largest source inventories, the UMLS LRABR (74.8%) and ADAM (68.0%). The metathesaurus demonstrated a sense (long form) micro-coverage of 99.6%, again a substantial increase compared to the UMLS LRABR (82.5%) and ADAM (55.4%).

conclusionsGiven the high coverage, harmonizing acronym sense inventories is a promising methodology to improve their comprehensiveness. Our method is automated, leverages the extensive resources already devoted to developing institution-specific inventories in the United States, and may help generalize sense inventories to institutions who lack the resources to develop them. Future work should address quality issues in source inventories and explore additional approaches to establishing synonymy.

Indexed as

Pattern Recognition, AutomatedUnified Medical Language SystemAlgorithmsDatabases, FactualHospitalsLanguageMedical InformaticsReproducibility of ResultsSemanticsSoftwareAcronymsandabbreviationsConsumer health informaticsKnowledge representationVocabulary and terminology

Identifiers

PMID30414475
PMCPMC6474250

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.