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Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.
Zhao, Yu; Zhang, Rusen; Zhong, Yi; Wang, Jingjing; Weng, Zuquan; Luo, Heng; Chen, Cunrong.
  • Zhao Y; College of Computer and Data Science, Fuzhou University, Fuzhou, China.
  • Zhang R; Centre for Big Data Research in Burns and Trauma, Fuzhou University, Fuzhou, China.
  • Zhong Y; Department of Cardiovascular Medicine, Affiliated Fuzhou First Hospital of Fujian Medical University, Fuzhou, China.
  • Wang J; College of Computer and Data Science, Fuzhou University, Fuzhou, China.
  • Weng Z; Centre for Big Data Research in Burns and Trauma, Fuzhou University, Fuzhou, China.
  • Luo H; Department of Critical Care Medicine, Union Hospital of Fujian Medical University, Fuzhou, China.
  • Chen C; Centre for Big Data Research in Burns and Trauma, Fuzhou University, Fuzhou, China.
Front Cell Infect Microbiol ; 12: 838749, 2022.
Article in English | MEDLINE | ID: covidwho-1822355
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Front Cell Infect Microbiol Year: 2022 Document Type: Article Affiliation country: Fcimb.2022.838749

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Front Cell Infect Microbiol Year: 2022 Document Type: Article Affiliation country: Fcimb.2022.838749