- Title: Subtitle
- 636: Identification of independent metabolic risk groups for necrotizing enterocolitis through machine learning
- Creators
- Scott P Oltman - Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CAElizabeth E Rogers - Department of Pediatrics, University of California San Francisco, San Francisco, CAMatthew Pantell - Department of Pediatrics, University of California San Francisco, San Francisco, CARebecca J Baer - California Preterm Birth Initiative, Benioff Children’s Hospital, University of California, San Francisco, CALarry Rand - California Preterm Birth Initiative, Benioff Children’s Hospital, University of California, San Francisco, CAKelli K Ryckman - Departments of Epidemiology and Pediatrics, University of Iowa, Iowa City, IAZachary Kastenberg - Department of Surgery, Stanford University School of Medicine, Stanford, CAKarl Sylvester - Department of Pediatrics, Stanford University, Stanford, CALaura Jeliffe-Pawlowski - Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA
- Resource Type
- Abstract
- Publication Details
- American journal of obstetrics and gynecology, Vol.216(1), pp.S372-S372
- DOI
- 10.1016/j.ajog.2016.11.370
- ISSN
- 0002-9378
- eISSN
- 1097-6868
- Publisher
- Elsevier Inc
- Language
- English
- Date published
- 01/2017
- Academic Unit
- Stead Family Department of Pediatrics; Epidemiology
- Record Identifier
- 9984216627902771
Abstract
636: Identification of independent metabolic risk groups for necrotizing enterocolitis through machine learning
American journal of obstetrics and gynecology, Vol.216(1), pp.S372-S372
01/2017
DOI: 10.1016/j.ajog.2016.11.370
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