Clinical utility assessment framework for machine learning-based fetal health classification in cardiotocography: an observational study.

Lee Y, Kim SY, Park H

DOI: 10.5468/ogs.25376
Published: 2026 Mar
Container: Obstetrics & gynecology science
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.5468/ogs.25376,
  title = {Clinical utility assessment framework for machine learning-based fetal health classification in cardiotocography: an observational study.},
  author = {Lee Y and Kim SY and Park H},
  year = {2026},
  journal = {Obstetrics \& gynecology science},
  doi = {10.5468/ogs.25376},
  url = {https://doi.org/10.5468/ogs.25376}
}

RIS

TY  - JOUR
TI  - Clinical utility assessment framework for machine learning-based fetal health classification in cardiotocography: an observational study.
AU  - Lee Y
AU  - Kim SY
AU  - Park H
PY  - 2026
JO  - Obstetrics & gynecology science
DO  - 10.5468/ogs.25376
UR  - https://doi.org/10.5468/ogs.25376
ER  -

APA

Y, L., SY, K., & H, P. (2026). Clinical utility assessment framework for machine learning-based fetal health classification in cardiotocography: an observational study.. Obstetrics & gynecology science. https://doi.org/10.5468/ogs.25376

Source records

pubmed · retrieved 2026-09-26T11:12:42.845Z