Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments

Milad Yousefi, Moslem Yousefi, Ricardo Poley Martins Ferreira, Joong Hoon Kim, Flavio S. Fogliatto

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses and receptionists need to be adjusted, while a number of constraints are to be considered at the same time. In this study, an efficient method based on agent-based simulation, machine learning and the genetic algorithm (GA) is presented to determine optimum resource allocation in emergency departments. GA can effectively explore the entire domain of all 19 variables and identify the optimum resource allocation through evolution and mimicking the survival of the fittest concept. A chaotic mutation operator is used in this study to boost GA performance. A model of the system needs to be run several thousand times through the GA evolution process to evaluate each solution, hence the process is computationally expensive. To overcome this drawback, a robust metamodel is initially constructed based on an agent-based system simulation. The simulation exhibits ED performance with various resource allocations and trains the metamodel. The metamodel is created with an ensemble of the adaptive neuro-fuzzy inference system (ANFIS), feedforward neural network (FFNN) and recurrent neural network (RNN) using the adaptive boosting (AdaBoost) ensemble algorithm. The proposed GA-based optimization approach is tested in a public ED, and it is shown to decrease the ALOS in this ED case study by 14%. Additionally, the proposed metamodel shows a 26.6% improvement compared to the average results of ANFIS, FFNN and RNN in terms of mean absolute percentage error (MAPE).

Original languageEnglish
JournalArtificial Intelligence in Medicine
DOIs
Publication statusAccepted/In press - 2017 Jan 1

Fingerprint

Adaptive boosting
Hospital Emergency Service
Genetic algorithms
Planning
Resource Allocation
Resource allocation
Recurrent neural networks
Feedforward neural networks
Fuzzy inference
Length of Stay
Health Care Sector
Molecular Evolution
Learning systems
Mathematical operators
Nurses
Mutation
Industry

Keywords

  • Adaboost ensemble metamodel
  • Chaotic genetic algorithm (GA)
  • Decision support system
  • Simulation-based optimization

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

Cite this

Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments. / Yousefi, Milad; Yousefi, Moslem; Ferreira, Ricardo Poley Martins; Kim, Joong Hoon; Fogliatto, Flavio S.

In: Artificial Intelligence in Medicine, 01.01.2017.

Research output: Contribution to journalArticle

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