ArticleFrontiers in cellular and infection microbiology2022
Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.
Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 21 citations in OpenAlex.
- Machine learning-driven identification of virulence determinants in Borrelia burgdorferi associated with human dissemination.PLoS computational biology · 2026Article
- CT Radiomics for the Early Identification of Fungal Co-infection in Immunocompromised Patients with Viral PneumoniaCurrent medical imaging · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.Biomolecules & biomedicine · 2025Article
- Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes.Communications medicine · 2024Article
- COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.Scientific reports · 2024Article
- Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.Frontiers in immunology · 2024Article
- Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.Research square · 2023Article
- Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.Frontiers in neurology · 2023Article
- Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa.Frontiers in artificial intelligence · 2023Article
- Identifying oral disease variables associated with pneumonia emergence by application of machine learning to integrated medical and dental big data to inform eHealth approaches.Frontiers in dental medicine · 2022Article
- Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.Frontiers in medicine · 2022Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Coronavirus Disease 2019 (COVID-19) has spread all over the world and impacted many people's lives. The characteristics of COVID-19 and other types of pneumonia have both similarities and differences, which confused doctors initially to separate and understand them. Here we presented a retrospective analysis for both COVID-19 and other types of pneumonia by combining the COVID-19 clinical data, eICU and MIMIC-III databases. Machine learning models, including logistic regression, random forest, XGBoost and deep learning neural networks, were developed to predict the severity of COVID-19 infections as well as the mortality of pneumonia patients in intensive care units (ICU). Statistical analysis and feature interpretation, including the analysis of two-level attention mechanisms on both temporal and non-temporal features, were utilized to understand the associations between different clinical variables and disease outcomes. For the COVID-19 data, the XGBoost model obtained the best performance on the test set (AUROC = 1.000 and AUPRC = 0.833). On the MIMIC-III and eICU pneumonia datasets, our deep learning model (Bi-LSTM_Attn) was able to identify clinical variables associated with death of pneumonia patients (AUROC = 0.924 and AUPRC = 0.802 for 24-hour observation window and 12-hour prediction window). The results highlighted clinical indicators, such as the lymphocyte counts, that may help the doctors to predict the disease progression and outcomes for both COVID-19 and other types of pneumonia.
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