July 22, 2024
Khodakaram Salimifard

Khodakaram Salimifard

Academic Rank: Associate professor
Address: Industrial Management Department, Business & Economics School, Persian Gulf University, Bushehr 75169
Degree: Ph.D in Operations Research
Phone: 07731222118
Faculty: School of Business and Economics


Title Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients
Type Article
COVID-19 pandemic ML in health systems Supervised learning Ensemble modeling
DOI https://doi.org/10.1007/s10479-022-04984-x
Researchers Sara Saadatmand (First researcher) , Khodakaram Salimifard (Second researcher) , Reza Mohammadi (Third researcher) , Alex Kuiper (Fourth researcher) , Maryam Marzban (Fifth researcher) , Akram Farhadi (Not in first six researchers)


The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient’s survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient’s likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.