Evidence map›Paper›PMID 23304381›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2012

Synonym, topic model and predicate-based query expansion for retrieving clinical documents.

Qing T Zeng, Doug Redd, Thomas Rindflesch, Jonathan Nebeker

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Framing Electronic Medical Records as Polylingual Documents in Query Expansion.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017
    Article
  8. Enhanced LexSynonym Acquisition for Effective UMLS Concept Mapping.Studies in health technology and informatics · 2017
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. 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

4 authors.

Qing T ZengVA Salt Lake City, UT, USA.
Doug Redd
Thomas Rindflesch
Jonathan Nebeker

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We present a study that developed and tested three query expansion methods for the retrieval of clinical documents. Finding relevant documents in a large clinical data warehouse is a challenging task. To address this issue, first, we implemented a synonym expansion strategy that used a few selected vocabularies. Second, we trained a topic model on a large set of clinical documents, which was then used to identify related terms for query expansion. Third, we obtained related terms from a large predicate database derived from Medline abstracts for query expansion. The three expansion methods were tested on a set of clinical notes. All three methods successfully achieved higher average recalls and average F-measures when compared with the baseline method. The average precisions and precision at 10, however, decreased with all expansions. Amongst the three expansion methods, the topic model-based method performed the best in terms of recall and F-measure.

Indexed as

Medical RecordsNatural Language ProcessingVocabulary, ControlledAbstracting and IndexingClinical MedicineInformation Storage and RetrievalMEDLINEUnified Medical Language System

Identifiers

PMID23304381
PMCPMC3540443

What OpenQuestion holds

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Registered trials

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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.