Publication in conference proceedings Q3
Predictive analysis in healthcare: emergency wait time prediction
Filipe Gonçalves (Gonçalves, F.); Rúben Pereira (Pereira, R.); Joao C Ferreira or Joao Ferreira (Ferreira, J.); José Braga Vasconcelos (Vasconcelos, J. B.); Fernando Melo (Melo, F.); Iria Velez (Velez, I.);
9th International Symposium on Ambient Intelligence, ISAmI 2018
Year (definitive publication)
2019
Language
English
Country
Switzerland
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Times Cited: 11

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Abstract
Emergency departments are an important area of a hospital, being the major entry point to the healthcare system. One of the most important issues regarding patient experience are the emergency department waiting times. In order to help hospitals improving their patient experience, the authors will perform a study where the Random Forest algorithm will be applied to predict emergency department waiting times. Using data from a Portuguese hospital from 2013 to 2017, the authors discretized the emergency waiting time in 5 different categories: “Really Low”, “Low”, “Average”, “High”, “Really High”. Plus, the authors considered as waiting time, the time from triage to observation. The authors expect to correctly evaluate the proposed classification algorithm efficiency and accuracy in order to be able to conclude if it is valuable when trying to predict ED waiting times.
Acknowledgements
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Keywords
Big data,Emergency department,Healthcare,Predictive analytics
  • Computer and Information Sciences - Natural Sciences
  • Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
Funding Records
Funding Reference Funding Entity
UID/MULTI/0446/2013 Fundação para a Ciência e a Tecnologia