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Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients.
Wong, Melissa S; Wells, Matthew; Zamanzadeh, Davina; Akre, Samir; Pevnick, Joshua M; Bui, Alex A T; Gregory, Kimberly D.
Affiliation
  • Wong MS; Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Wells M; Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Zamanzadeh D; Enterprise Data Intelligence, Cedars-Sinai Medical Center, Los Angeles, California.
  • Akre S; Medical and Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California.
  • Pevnick JM; Medical and Imaging Informatics (MII) Group, Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California.
  • Bui AAT; Division of Informatics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Gregory KD; Division of General Internal Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
Am J Perinatol ; 2022 Dec 29.
Article in En | MEDLINE | ID: mdl-35752169
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Am J Perinatol Year: 2022 Type: Article
Fulltext
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: Am J Perinatol Year: 2022 Type: Article