ReviewJournal of medical virology2022
Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir.
Review in Journal of medical virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- MRI- and report-based multimodal model with SHAP-based explanation for preoperative prediction of deep stromal invasion in early-stage cervical cancer.Insights into imaging · 2026Article
- A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes.International journal of clinical pharmacy · 2025Article
- A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease.Heliyon · 2024Article
- Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach.Journal of clinical medicine · 2024Article
- Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomics Model for Preoperative Predicting the Deep Stromal Invasion in Patients with Early Cervical Cancer.Journal of imaging informatics in medicine · 2024Article
- Machine learning models for early prediction of potassium lowering effectiveness and adverse events in patients with hyperkalemia.Scientific reports · 2024Article
- Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19.Diagnostics (Basel, Switzerland) · 2023Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Validation of a specialized evaluation system for COVID-19 in Japan: A retrospective, multicenter cohort study.Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy · 2023Article
- Evaluation of Remdesivir Utilization Pattern in Critically Ill Patients With COVID-19 in Jazan Province.Cureus · 2023Article
- Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study.Frontiers in medicine · 2023Article
- Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.Annals of operations research · 2022Article
- Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.Journal of clinical medicine · 2022Article
- Impact of System and Diagnostic Errors on Medical Litigation Outcomes: Machine Learning-Based Prediction Models.Healthcare (Basel, Switzerland) · 2022Article
- The association of anticoagulation before admission and survival of patients with COVID-19.Journal of cardiology · 2022Article
- The association of statins use with survival of patients with COVID-19.Journal of cardiology · 2022 · on this mapObservational
- Prediction of in-hospital mortality with machine learning for COVID-19 patients treated with steroid and remdesivir.Journal of medical virology · 2022Review
- Introduction to Clinical Prediction Models.Annals of clinical epidemiology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to create the prediction model of in-hospital mortality using machine learning methods for patients with coronavirus disease 2019 (COVID-19) treated with steroid and remdesivir. We reviewed 1571 hospitalized patients with laboratory confirmed COVID-19 from the Mount Sinai Health System treated with both steroids and remdesivir. The important variables associated with in-hospital mortality were identified using LASSO (least absolute shrinkage and selection operator) and SHAP (SHapley Additive exPlanations) through the light gradient boosting model (GBM). The data before February 17th, 2021 (N = 769) was randomly split into training and testing datasets; 80% versus 20%, respectively. Light GBM models were created with train data and area under the curves (AUCs) were calculated. Additionally, we calculated AUC with the data between February 17th, 2021 and March 30th, 2021 (N = 802). Of the 1571 patients admitted due to COVID-19, 331 (21.1%) died during hospitalization. Through LASSO and SHAP, we selected six important variables; age, hypertension, oxygen saturation, blood urea nitrogen, intensive care unit admission, and endotracheal intubation. AUCs using training and testing datasets derived from the data before February 17th, 2021 were 0.871/0.911. Additionally, the light GBM model has high predictability for the latest data (AUC: 0.881) (https://risk-model.herokuapp.com/covid). A high-value prediction model was created to estimate in-hospital mortality for COVID-19 patients treated with steroid and remdesivir.
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