scholarly journals Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation (Preprint)

10.2196/32726 ◽  
2021 ◽  
Author(s):  
Jeong Min Kim ◽  
Hwa Kyung Lim ◽  
Jae-Hyeon Ahn ◽  
Kyoung Hwa Lee ◽  
Kwang Suk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Healthcare Resources ◽  
Event Simulation ◽  
Threshold Optimization ◽  
Resources Development

Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation (Preprint)

2021 ◽  
Author(s):  
Jeong Min Kim ◽  
Hwa Kyung Lim ◽  
Jae-Hyeon Ahn ◽  
Kyoung Hwa Lee ◽  
Kwang Suk Lee ◽  
...  
Keyword(s):  
Prediction Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Mortality Rates ◽  
Clinical Severity ◽  
Ordinal Scale ◽  
Event Simulation ◽  
Hypothetical Patient ◽  
Optimal Thresholds ◽  
Threshold Optimization

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented burden on healthcare systems. OBJECTIVE To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining the healthcare system capacity. METHODS A nationwide sample of 5601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the patients’ maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the extent of the model performance’s maintenance when clinical and laboratory variables are eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find the optimal threshold within limited resource environments that minimizes mortality rates. RESULTS The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model’s performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Our prediction model was provided online for clinical implementation. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1% compared to the conventional Youden Index. CONCLUSIONS Our adaptive triage model and its threshold optimization capability reveal that COVID-19 management can be integrated using both medical and healthcare management sectors to guarantee maximum treatment efficacy.

scholarly journals Optimal Triage for COVID-19 Patients Under Limited Intensive Care Unit Capacity: Development of a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation

2021 ◽  
Author(s):  
Jeong Min Kim ◽  
Hwa Kyung Lim ◽  
Jae-Hyeon Ahn ◽  
Kyoung Hwa Lee ◽  
Kwang Suk Lee ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Mortality Rates ◽  
Data Availability ◽  
Clinical Severity ◽  
Ordinal Scale ◽  
Event Simulation ◽  
Hypothetical Patient ◽  
Optimal Thresholds ◽  
Threshold Optimization

Abstract Background: The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented burden on healthcare systems. To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining the healthcare system capacity.Methods: A nationwide sample of 5601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the patients’ maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate the extent of the model performance’s maintenance when clinical and laboratory variables are eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find the optimal threshold within limited resource environments that minimizes mortality rates.Results: The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model’s performance, the AUROC of the reduced model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1% compared to the conventional Youden Index.Conclusions: Our adaptive triage model and its threshold optimization capability reveal that COVID-19 management can be integrated using both medical and healthcare management sectors to guarantee maximum treatment efficacy.

scholarly journals A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

2021 ◽  
Vol 11 (24) ◽  
pp. 11725
Author(s):  
Eman Azab ◽  
Mohamed Nafea ◽  
Lamia A. Shihata ◽  
Maggie Mashaly
Keyword(s):  
Machine Learning ◽  
Discrete Event Simulation ◽  
Flow Shop ◽  
Discrete Event ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Simulation Approach ◽  
Dynamic Flow ◽  
Event Simulation ◽  
Minimum Makespan

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.

Sign in / Sign up

Export Citation Format

Share Document