iScience (Apr 2022)
A data-driven health index for neonatal morbidities
- Davide De Francesco,
- Yair J. Blumenfeld,
- Ivana Marić,
- Jonathan A. Mayo,
- Alan L. Chang,
- Ramin Fallahzadeh,
- Thanaphong Phongpreecha,
- Alex J. Butwick,
- Maria Xenochristou,
- Ciaran S. Phibbs,
- Neda H. Bidoki,
- Martin Becker,
- Anthony Culos,
- Camilo Espinosa,
- Qun Liu,
- Karl G. Sylvester,
- Brice Gaudilliere,
- Martin S. Angst,
- David K. Stevenson,
- Gary M. Shaw,
- Nima Aghaeepour
Affiliations
- Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Yair J. Blumenfeld
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
- Jonathan A. Mayo
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Alex J. Butwick
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
- Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Ciaran S. Phibbs
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; Health Economics Resource Center, VA Palo Alto Health Care System, Stanford, CA 94305, USA
- Neda H. Bidoki
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Qun Liu
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Karl G. Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
- Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA
- David K. Stevenson
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Gary M. Shaw
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA; Corresponding author
- Journal volume & issue
-
Vol. 25,
no. 4
p. 104143
Abstract
Summary: Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.
