title: Medical decision support using machine learning for early detection of late-onset neonatal sepsis.
abstract: OBJECTIVE
  The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).
  
  
  METHODS
  The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms.
  
  
  METHODS
  We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms.
  
  
  RESULTS
  The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate.
  
  
  CONCLUSIONS
  Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.
authors.affiliation: Department of Medicine, University of New Mexico, Albuquerque, New Mexico, USA.
authors.forename: Subramani
authors.initials: S
authors.lastname: Mani
authors.forename: Asli
authors.initials: A
authors.lastname: Ozdas
authors.forename: Constantin
authors.initials: C
authors.lastname: Aliferis
authors.forename: Huseyin Atakan
authors.initials: HA
authors.lastname: Varol
authors.forename: Qingxia
authors.initials: Q
authors.lastname: Chen
authors.forename: Randy
authors.initials: R
authors.lastname: Carnevale
authors.forename: Yukun
authors.initials: Y
authors.lastname: Chen
authors.forename: Joann
authors.initials: J
authors.lastname: Romano-Keeler
authors.forename: Hui
authors.initials: H
authors.lastname: Nian
authors.forename: Jörn-Hendrik
authors.initials: JH
authors.lastname: Weitkamp
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D001185:Artificial Intelligence; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D057286:Electronic Health Records; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000175:diagnosis* / Q000188:drug therapy
chemical_list: D000900:Anti-Bacterial Agents
keywords: Decision Support; Early Detection; Electronic Medical Records; Machine Learning; Neonatal Sepsis; Predictive Models
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