A data-driven health index for neonatal morbidities

iScience

Yair J. Blumenfeld¹  Camilo Espinosa¹  Neda H. Bidoki¹  Martin S. Angst¹  Alex J. Butwick¹  Qun Liu²  Ivana Marić³  Martin Becker³  David K. Stevenson³  Maria Xenochristou³  Nima Aghaeepour⁴  Karl G. Sylvester⁵  Anthony Culos⁵  Ciaran S. Phibbs⁵  Brice Gaudilliere⁵  Alan L. Chang⁵  Davide De Francesco⁵  Thanaphong Phongpreecha⁵  Jonathan A. Mayo⁶  Gary M. Shaw⁷  Ramin Fallahzadeh⁷ 

[1] Department of Biomedical Data Sciences, Stanford University, Stanford, CA 94305, USA;Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA;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;Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA 94305, USA;Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA;Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA;

关键词: Biological sciences;    Cell biology;    Molecular biology;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown