Evidence map›Paper›PMID 16929043›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA

Using regular expressions to abstract blood pressure and treatment intensification information from the text of physician notes.

Alexander Turchin, Nikheel S Kolatkar, Richard W Grant, Eric C Makhni, Merri L Pendergrass, Jonathan S Einbinder

Open access · bronzeAbstract readEvaluation Study
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 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 12% of its field
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

50 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.

  1. A systematic literature review of automated clinical coding and classification systems.Journal of the American Medical Informatics Association : JAMIA
    Pooled it
  2. Trial
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  6. Natural Language Processing in Nephrology.Advances in chronic kidney disease · 2022
    Review
  7. Article
  8. Observational
  9. Article
  10. Article
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  12. A deep-learning based system for accurate extraction of blood pressure data in clinical narratives.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020
    Article
  13. Article
  14. Article
  15. Observational
  16. Article
  17. Extracting Healthcare Quality Information from Unstructured Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017
    Article
  18. Shock in the emergency department; a 12 year population based cohort study.Scandinavian journal of trauma, resuscitation and emergency medicine · 2016
    Article
  19. Article
  20. 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

6 authors at 4 institutions in 1 country.

Alexander TurchinClinical Informatics Research and Development, Partners HealthCare System, 93 Worcester Street, Suite 201, Wellesley, MA 02481, USA. aturchin@partners.org
Nikheel S Kolatkar
Richard W Grant
Eric C Makhni
Merri L Pendergrass
Jonathan S Einbinder
Brigham and Women's Hospital · USHarvard University · USMassachusetts General Hospital · USMass General Brigham · US

Funding

TRAINING PROGRAM IN HYPERTENSIONT32HL007609 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI ADLER, GAIL KURR · 1985 to 2022
$10.2M
Overcoming Barriers to Evidence-Based Diabetes CareK23DK067452 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI GRANT, RICHARD W · 2005 to 2009
$653k
NHLBI NIH HHS T32 HL007609NHLBI NIH HHS T32HL007609NIDDK NIH HHS K23 DK067452
6 · The paper itself

Abstract

This case study examined the utility of regular expressions to identify clinical data relevant to the epidemiology of treatment of hypertension. We designed a software tool that employed regular expressions to identify and extract instances of documented blood pressure values and anti-hypertensive treatment intensification from the text of physician notes. We determined sensitivity, specificity and precision of identification of blood pressure values and anti-hypertensive treatment intensification using a gold standard of manual abstraction of 600 notes by two independent reviewers. The software processed 370 Mb of text per hour, and identified elevated blood pressure documented in free text physician notes with sensitivity and specificity of 98%, and precision of 93.2%. Anti-hypertensive treatment intensification was identified with sensitivity 83.8%, specificity of 95.0%, and precision of 85.9%. Regular expressions can be an effective method for focused information extraction tasks related to high-priority disease areas such as hypertension.

Indexed as

Blood PressureMedical RecordsNatural Language ProcessingSoftwareHumansHypertensionPhysiciansTerminology as Topic

Identifiers

PMID16929043
PMCPMC1656954
OpenAlexW2143966396

What OpenQuestion holds

Textmetadata
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.