Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia

Alabbad, Dina A.; Almuhaideb, Abdullah M.; Alsunaidi, Shikah J.; Alqudaihi, Kawther S.; Alamoudi, Fatimah A.; Alhobaishi, Maha K.; Alaqeel, Naimah A.; Alshahrani, Mohammed

doi:10.1016/j.imu.2022.100937

Cited by 25 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Common machine learning classification models are Bayes network (BN) models, support vector machine (SVM), Radial basis function (RBF) tree, decision table, and naive Bayes. ML models have demonstrated high predictive performance in predicting LOS after pediatric heart transplantation and patients with COVID-19 ( 55 , 56 ), which is a future direction for our research. In the meantime, studies should also focus on interventions to effectively reduce LOS-NICU and improve short- and long-term newborn outcomes.…”

Section: Discussionmentioning

confidence: 91%

Risk factors for length of NICU stay of newborns: A systematic review

Song

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

BackgroundThe improvement in survival of preterm infants is accompanied by an increase in neonatal intensive care unit (NICU) admissions. Prolonged length of stay in the NICU (LOS-NICU) increases the incidence of neonatal complications and even mortality and places a significant economic burden on families and strain on healthcare systems. This review aims to identify risk factors influencing LOS-NICU of newborns and to provide a basis for interventions to shorten LOS-NICU and avoid prolonged LOS-NICU.MethodsA systematic literature search was conducted in PubMed, Web of Science, Embase, and Cochrane library for studies that were published in English from January 1994 to October 2022. The PRISMA guidelines were followed in all phases of this systematic review. The Quality in Prognostic Studies (QUIPS) tool was used to assess methodological quality.ResultsTwenty-three studies were included, 5 of which were of high quality and 18 of moderate quality, with no low-quality literature. The studies reported 58 possible risk factors in six broad categories (inherent factors; antenatal treatment and maternal factors; diseases and adverse conditions of the newborn; treatment of the newborn; clinical scores and laboratory indicators; organizational factors).ConclusionsWe identified several of the most critical risk factors affecting LOS-NICU, including birth weight, gestational age, sepsis, necrotizing enterocolitis, bronchopulmonary dysplasia, and retinopathy of prematurity. As only a few high-quality studies are available at present, well-designed and more extensive prospective studies investigating the risk factors affecting LOS-NICU are still needed in the future.

show abstract

Section: Discussionmentioning

confidence: 91%

Risk factors for length of NICU stay of newborns: A systematic review

Song

et al. 2023

Front. Pediatr.

View full text Add to dashboard Cite

show abstract

“…Machine learning models have tried to improve predictive values. Some models have focused only on mortality in the ICU or days of ICU hospitalization [ [27] , [28] , [29] , [30] ].…”

Section: Discussionmentioning

confidence: 99%

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

Kokoris

Gavriilaki

Tsoukalas

et al. 2023

Clinical Immunology

View full text Add to dashboard Cite

“…13 In one study by Alabbad et al, the LOS of COVID ICU admissions was predicted using ML models with an accuracy of 94.16%. 14 Jorge et al demonstrated ML models could accurately predict SLE hospitalization and identify predictors of disease severity. 15 Based on this approach, our study's results underscore the potential of ML models for improving LOS prediction accuracy and guiding resource allocation in patients with SLE.…”

Section: Discussionmentioning

confidence: 99%

Machine learning: Predicting hospital length of stay in patients admitted for lupus flares

Grovu,

Huo,

Nguyen

et al. 2023

Lupus

View full text Add to dashboard Cite

Background Although rare, severe systemic lupus erythematosus (SLE) flares requiring hospitalization account for most of the direct costs of SLE care. New machine learning (ML) methods may optimize lupus care by predicting which patients will have a prolonged hospital length of stay (LOS). Our study uses a machine learning approach to predict the LOS in patients admitted for lupus flares and assesses which features prolong LOS. Methods Our study sampled 5831 patients admitted for lupus flares from the National Inpatient Sample Database 2016–2018 and collected 90 demographics and comorbidity features. Four machine learning (ML) models were built (XGBoost, Linear Support Vector Machines, K Nearest Neighbors, and Logistic Regression) to predict LOS, and their performance was evaluated using multiple metrics, including accuracy, receiver operator area under the curve (ROC-AUC), precision-recall area under the curve (PR- AUC), and F1-score. Using the highest-performing model (XGBoost), we assessed the feature importance of our input features using Shapley value explanations (SHAP) to rank their impact on LOS. Results Our XGB model performed the best with a ROC-AUC of 0.87, PR-AUC of 0.61, an F1 score of 0.56, and an accuracy of 95%. The features with the most significant impact on the model were “the need for a central line,” “acute dialysis,” and “acute renal failure.” Other top features include those related to renal and infectious comorbidities. Conclusion Our results were consistent with the established literature and showed promise in ML over traditional methods of predictive analyses, even with rare rheumatic events such as lupus flare hospitalizations.

show abstract

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia

Cited by 25 publications

References 32 publications

Risk factors for length of NICU stay of newborns: A systematic review

Risk factors for length of NICU stay of newborns: A systematic review

Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices

Machine learning: Predicting hospital length of stay in patients admitted for lupus flares

Contact Info

Product

Resources

About