Machine learning predictive model for severe COVID-19

Kang, Jianhong; Chen, Ting; Luo, Honghe; Luo, Yi; Du, Guipeng; Jiming-Yang, Mia

doi:10.1016/j.meegid.2021.104737

Cited by 31 publications

(16 citation statements)

References 29 publications

(26 reference statements)

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“…Different ML models have been proposed to predict risk of developing severe complications and mortality (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27). This is important since there are limited resources compared to the increasing number of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan

Garg

Vinod

et al. 2021

Front. Public Health

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The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan

Garg

Vinod

et al. 2021

Front. Public Health

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“…(2020) found that the white blood cell counts (WBCs) and procalcitonin in severe COVID-19 patients increased significantly, indicating their clinical predictability of COVID-19 severity. The increase of blood urea nitrogen (BUN) can lead to renal function injury ( STARK, 1980 ), which may deteriorate the severity of COVID-19 ( Kang et al., 2021 ). Lippi and Plebani (2020) reported the procalcitonin levels of severe COVID-19 patients were twice as much as non-severe COVID-19 patients, and Oblokulov et al.…”

Section: Discussionmentioning

confidence: 99%

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

Zhao

Zhang

Zhong

et al. 2022

Front. Cell. Infect. Microbiol.

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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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“…The authors of [ 39 ] presented a neural network model for the detection of the severity of COVID-19 in small data samples from the Tongji Medical College of Huazhong University of Science and Technology, Hubei, in collaboration with the Tumor Center of Union Hospital, China. The authors evaluated the severity of COVID-19 on 151 images after selecting features.…”

Section: Methodsmentioning

confidence: 99%

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli

Damaševičius

Maskeliūnas

et al. 2022

Sensors

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Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.

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Machine learning predictive model for severe COVID-19

Cited by 31 publications

References 29 publications

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

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

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

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