ArticleJournal of medical Internet research2021
Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- Predicting COVID-19 Mortality Risk Among Cardiovascular Disease Patients Using Artificial Intelligence Algorithms: A Retrospective Study on Clinical Data.Health science reports · 2026Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Predictors of COVID-19 severity among pregnant patients.Bosnian journal of basic medical sciences · 2022Article
- Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic.Journal of clinical medicine · 2022Article
- Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.BMJ open · 2022Article
- Learning and Predicting from Dynamic Models for COVID-19 Patient Monitoring.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Article
- Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling.Frontiers in immunology · 2022Article
- A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile.Frontiers in cellular and infection microbiology · 2022Article
- Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.Emerging topics in life sciences · 2021Article
- Deep forest model for diagnosing COVID-19 from routine blood tests.Scientific reports · 2021Article
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6 authors.
Funding
Abstract
backgroundThe COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality.
objectiveThe goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression.
methodsThe clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU).
resultsThe XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO
conclusionsTogether, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
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