title: Machine learning and new vital signs monitoring in civilian en route care: A systematic review of the literature and future implications for the military.
abstract: BACKGROUND
  Although air transport medical services are today an integral part of trauma systems in most developed countries, to date, there are no reviews on recent innovations in civilian en route care. The purpose of this systematic review was to identify potential machine learning and new vital signs monitoring technologies in civilian en route care that could help close civilian and military capability gaps in monitoring and the early detection and treatment of various trauma injuries.
  
  
  METHODS
  MEDLINE, the Cochrane Database of Systematic Reviews, and citation review of relevant primary and review articles were searched for studies involving civilian en route care, air medical transport, and technologies from January 2005 to November 2015. Data were abstracted on study design, population, year, sponsors, innovation category, details of technologies, and outcomes.
  
  
  RESULTS
  Thirteen observational studies involving civilian medical transport met inclusion criteria. Studies either focused on machine learning and software algorithms (n = 5), new vital signs monitoring (n = 6), or both (n = 2). Innovations involved continuous digital acquisition of physiologic data and parameter extraction. Importantly, all studies (n = 13) demonstrated improved outcomes where applicable and potential use during civilian and military en route care. However, almost all studies required further validation in prospective and/or randomized controlled trials.
  
  
  CONCLUSIONS
  Potential machine learning technologies and monitoring of novel vital signs such as heart rate variability and complexity in civilian en route care could help enhance en route care for our nation's war fighters. In a complex global environment, they could potentially fill capability gaps such as monitoring and the early detection and treatment of various trauma injuries. However, the impact of these innovations and technologies will require further validation before widespread acceptance and prehospital use.
  
  
  METHODS
  Systematic review, level V.
authors.affiliation: From the US Army Institute of Surgical Research, Fort Sam Houston, San Antonio, Texas.
authors.forename: Nehemiah T
authors.initials: NT
authors.lastname: Liu
authors.forename: Jose
authors.initials: J
authors.lastname: Salinas
mesh_terms: D017732:Air Ambulances; D004632:Emergency Medical Services / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008991:Monitoring, Physiologic / Q000379:methods*; D012984:Software; D014187:Transportation of Patients; D055986:Vital Signs