Evidence map›Paper›PMID 34712103›Full record

ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2021

IDENTIFICATION OF UTERINE CONTRACTIONS BY AN ENSEMBLE OF GAUSSIAN PROCESSES.

Liu Yang, Cassandra Heiselman, J Gerald Quirk, Petar M Djurić

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. UNSUPERVISED CLUSTERING AND ANALYSIS OF CONTRACTION-DEPENDENT FETAL HEART RATE SEGMENTS.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2022
    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.

Liu YangDepartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
Cassandra HeiselmanDepartment of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA.
J Gerald QuirkDepartment of Obstetrics, Gynecology and Reproductive Medicine, Stony Brook, NY 11794, USA.
Petar M DjurićDepartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA.

Funding

Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean DeliveriesR01HD097188 · NICHD · STATE UNIVERSITY NEW YORK STONY BROOK · PI DJURIC, PETAR M, QUIRK, J GERALD · 2019 to 2023
$3.2M
NICHD NIH HHS R01 HD097188
6 · The paper itself

Abstract

Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.

Indexed as

ensemble learningGaussian process latent variable modeluterine contraction

Identifiers

PMID34712103
PMCPMC8547336

What OpenQuestion holds

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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.