Evidence map›Paper›PMID 29128973›Full record

ArticleJournal of medical systems2017

Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.

Nafissa Sadi-Ahmed, Baya Kacha, Hamza Taleb, Malika Kedir-Talha

Abstract read
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In one paragraph

Article in Journal of medical systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.9field-weighted citation impact, top 6% 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

11 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022
    Article
  7. Article
  8. Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022
    Article
  9. Review
  10. Review
  11. 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

4 authors at 1 institution in 1 country.

Nafissa Sadi-AhmedFaculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria. n.sadiahmed@gmail.com.ORCID http://orcid.org/0000-0001-8845-3861
Baya KachaFaculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria.
Hamza TalebFaculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria.
Malika Kedir-TalhaFaculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria.
University of Sciences and Technology Houari Boumediene · DZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37

Indexed as

Signal Processing, Computer-AssistedSupport Vector MachineAlgorithmsElectromyographyFemaleHumansPregnancyPremature BirthReproducibility of ResultsUterusElectrohysterographic EHG signalHuang-hilbert transform (HHT)Intrinsic mode function IMFPreterm childbirth threatSupport vector machine (SVM)Uterine EMG signal

Identifiers

PMID29128973
OpenAlexW2768049785

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.