- Title: Subtitle
- 301: Identification of unique risk groups for preterm birth using routinely screened biomarkers and machine learning
- Creators
- Laura Jelliffe-Pawlowski - University of California San Francisco School of Medicine; Department of Epidemiology & Biostatistics, San Francisco, CAScott P Oltman - University of California San Francisco School of Medicine; Department of Epidemiology & Biostatistics, San Francisco, CARebecca J Baer - UCSF California Preterm Birth Initiative, San Francisco, CAKelli K Ryckman - University of Iowa; Department of Epidemiology, Iowa City, IALarry Rand - UCSF California Preterm Birth Initiative, San Francisco, CA
- Resource Type
- Abstract
- Publication Details
- American journal of obstetrics and gynecology, Vol.216(1), pp.S185-S185
- DOI
- 10.1016/j.ajog.2016.11.559
- 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
- 9984216626102771
Abstract
301: Identification of unique risk groups for preterm birth using routinely screened biomarkers and machine learning
American journal of obstetrics and gynecology, Vol.216(1), pp.S185-S185
01/2017
DOI: 10.1016/j.ajog.2016.11.559
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