A Machine Learning Model for Triage in Lean Pediatric Emergency Departments

  • William Caicedo-TorresEmail author
  • Gisela García
  • Hernando Pinzón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


High demand periods and under-staffing due to financial constraints cause Emergency Departments (EDs) to frequently exhibit over-crowding and slow response times to provide adequate patient care. In response, Lean Thinking has been applied to help alleviate some of these issues and improve patient handling, with success. Lean approaches in EDs include separate patient streams, with low-complexity patients treated in a so-called Fast Track, in order to reduce total waiting time and to free-up capacity to treat more complicated patients in a timely manner. In this work we propose the use of Machine Learning techniques in a Lean Pediatric ED to correctly predict which patients should be admitted to the Fast Track, given their signs and symptoms. Charts from 1205 patients of the emergency department of Hospital Napoleón Franco Pareja in Cartagena - Colombia, were used to construct a dataset and build several predictive models. Validation and test results are promising and support the validity of this approach and further research on the subject.


Machine learning Triage Emergency department Lean Fast track Neural networks SVM Logistic regression PCA 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • William Caicedo-Torres
    • 1
    Email author
  • Gisela García
    • 1
  • Hernando Pinzón
    • 2
  1. 1.Department of Computer ScienceUniversidad Tecnológica de BolívarCartagenaColombia
  2. 2.Hospital Infantil Napoleón Franco ParejaCartagenaColombia

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