Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients

• Published in: Frontiers in cellular and infection microbiology Vol. 12; p. 838749
• Main Authors: Zhao, Yu, Zhang, Rusen, Zhong, Yi, Wang, Jingjing, Weng, Zuquan, Luo, Heng, Chen, Cunrong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 19.04.2022
• Subjects: attention mechanism; Cellular and Infection Microbiology; clinical indicators; COVID-19 - diagnosis; Humans; Intensive Care Units; Machine Learning; pneumonia; Pneumonia - diagnosis; Retrospective Studies; statistical analysis; the coronavirus disease 2019; clinical indicators; pneumonia; machine learning; statistical analysis; attention mechanism; the coronavirus disease 2019
• ISSN: 2235-2988; 2235-2988
• DOI: 10.3389/fcimb.2022.838749

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.