Evidence map›Paper›PMID 26438655›Full record

ArticleJournal of clinical monitoring and computing2016

Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.

Marilyn Hravnak, Lujie Chen, Artur Dubrawski, Eliezer Bose, Gilles Clermont, Michael R Pinsky

Abstract read
In one paragraph

Article in Journal of clinical monitoring and computing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Weakly Supervised Classification of Vital Sign Alerts as Real or Artifact.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    Article
  9. Article
  10. Article
  11. Article
  12. Applying machine learning to continuously monitored physiological data.Journal of clinical monitoring and computing · 2019
    Review
  13. Article
  14. Article
  15. Sensor, Signal, and Imaging Informatics.Yearbook of medical informatics · 2017
    Review
  16. Review
  17. Article
  18. Article
  19. Review
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.

Marilyn HravnakDepartment of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA. mhra@pitt.edu.
Lujie ChenCarnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
Artur DubrawskiCarnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
Eliezer BoseDepartment of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA.
Gilles ClermontDepartment of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA, USA.
Michael R PinskyDepartment of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA, USA.

Funding

Predicting Patient Instability Noninvasively for Nursing Care-Two (PPINNC-2)R01NR013912 · NINR · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI AL-ZAITI, SALAH S, CLERMONT, GILLES · 2012 to 2025
$7.0M
NINR NIH HHS R01 NR013912
6 · The paper itself

Abstract

Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby "cleaning" such data for future modeling. 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data [heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO

Indexed as

Clinical AlarmsHeart RateMonitoring, PhysiologicAdultAgedAlgorithmsArea Under CurveArtifactsBlood PressureData Interpretation, StatisticalData MiningDecision Support Systems, ClinicalFemaleHospital Information SystemsHumansMachine LearningArchived dataArtifactBig-dataCardiorespiratory instabilityMachine learningPhysiologic monitoringVital signs

Identifiers

PMID26438655
PMCPMC4821824

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.