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Deep learning predicts extreme preterm birth from electronic health records.
Gao, Cheng; Osmundson, Sarah; Velez Edwards, Digna R; Jackson, Gretchen Purcell; Malin, Bradley A; Chen, You.
Afiliación
  • Gao C; Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Osmundson S; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Velez Edwards DR; Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Obstetrics and Gynecology, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
  • Jackson GP; Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Departments of Pediatric Surgery and Pediatrics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Evaluation Research Center, IBM Watson Health, Cambridge,
  • Malin BA; Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical Engineering & Computer Science, School of Engine
  • Chen Y; Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address: you.chen@vanderbilt.edu.
J Biomed Inform ; 100: 103334, 2019 12.
Article en En | MEDLINE | ID: mdl-31678588
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Recien Nacido Extremadamente Prematuro / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Año: 2019 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Registros Electrónicos de Salud / Recien Nacido Extremadamente Prematuro / Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans / Newborn Idioma: En Año: 2019 Tipo del documento: Article