An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study
Dublin Core
Title
An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study
Subject
Artificial Intelligence (AI), Random Forest (RF), Discrete-Event-Simulation (DES), Emergency Department
(ED), Mechanical ventilation, Healthcare
(ED), Mechanical ventilation, Healthcare
Description
Background Shortages of mechanical ventilation have become a constant problem in Emergency Departments
(EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health
complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust
methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of
ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES)
to design effective interventions ensuring the high availability of ventilators for patients needing these devices.
Methods First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-
affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model
to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different
interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European
hospital group was used to validate the proposed methodology.
Results The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity
of the AI model was 93.08% (95% confidence interval, [88.46 −96.26%]), whilst the specificity was 85.45% [77.45
−91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 −95.13%) and 87.85%
(80.12 −93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 −100%). Finally,
the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource
capacity strategy.
Conclusions Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the
waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.
Keywords Artificial Intelligence (AI), Random Forest (RF), Discrete-Event-Simulation (DES), Emergency Department
(ED), Mechanical ventilation, Healthcare
(EDs), thereby affecting the timely deployment of medical interventions that counteract the severe health
complications experienced during respiratory disease seasons. It is then necessary to count on agile and robust
methodological approaches predicting the expected demand loads to EDs while supporting the timely allocation of
ventilators. In this paper, we propose an integration of Artificial Intelligence (AI) and Discrete-event Simulation (DES)
to design effective interventions ensuring the high availability of ventilators for patients needing these devices.
Methods First, we applied Random Forest (RF) to estimate the mechanical ventilation probability of respiratory-
affected patients entering the emergency wards. Second, we introduced the RF predictions into a DES model
to diagnose the response of EDs in terms of mechanical ventilator availability. Lately, we pretested two different
interventions suggested by decision-makers to address the scarcity of this resource. A case study in a European
hospital group was used to validate the proposed methodology.
Results The number of patients in the training cohort was 734, while the test group comprised 315. The sensitivity
of the AI model was 93.08% (95% confidence interval, [88.46 −96.26%]), whilst the specificity was 85.45% [77.45
−91.45%]. On the other hand, the positive and negative predictive values were 91.62% (86.75 −95.13%) and 87.85%
(80.12 −93.36%). Also, the Receiver Operator Characteristic (ROC) curve plot was 95.00% (89.25 −100%). Finally,
the median waiting time for mechanical ventilation was decreased by 17.48% after implementing a new resource
capacity strategy.
Conclusions Combining AI and DES helps healthcare decision-makers to elucidate interventions shortening the
waiting times for mechanical ventilators in EDs during respiratory disease epidemics and pandemics.
Keywords Artificial Intelligence (AI), Random Forest (RF), Discrete-Event-Simulation (DES), Emergency Department
(ED), Mechanical ventilation, Healthcare
Creator
Miguel Ortiz-Barrios1,2*, Antonella Petrillo3
, Sebastián Arias-Fonseca2
, Sally McClean4
, Fabio de Felice3
, Chris Nugent4
and Sheyla-Ariany Uribe-López5
, Sebastián Arias-Fonseca2
, Sally McClean4
, Fabio de Felice3
, Chris Nugent4
and Sheyla-Ariany Uribe-López5
Source
https://doi.org/10.1186/s12245-024-00626-0
Date
2024
Contributor
Peri Irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Miguel Ortiz-Barrios1,2*, Antonella Petrillo3
, Sebastián Arias-Fonseca2
, Sally McClean4
, Fabio de Felice3
, Chris Nugent4
and Sheyla-Ariany Uribe-López5, “An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study,” Repository Horizon University Indonesia, accessed April 11, 2026, https://repository.horizon.ac.id/items/show/12333.