Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data

Souza, Fernanda Sumika Hojo de; Hojo-Souza, Natália Satchiko; Santos, Edimilson Batista dos; Silva, Cristiano M.; Guidoni, Daniel L.

doi:10.3389/frai.2021.579931

Cited by 29 publications

(12 citation statements)

References 33 publications

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“…Various artificial intelligence algorithms have been used to predict death or hospitalization in the ICU following COVID-19 [ 24 , 25 ]. Among the various machine learning models, the most widely used for classification are XGBoost, linear regression, support vector machine, decision tree, random forest, and neural network convolution.…”

Section: Discussionmentioning

confidence: 99%

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Giotta

Trerotoli

Palmieri

et al. 2022

IJERPH

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Many studies have identified predictors of outcomes for inpatients with coronavirus disease 2019 (COVID-19), especially in intensive care units. However, most retrospective studies applied regression methods to evaluate the risk of death or worsening health. Recently, new studies have based their conclusions on retrospective studies by applying machine learning methods. This study applied a machine learning method based on decision tree methods to define predictors of outcomes in an internal medicine unit with a prospective study design. The main result was that the first variable to evaluate prediction was the international normalized ratio, a measure related to prothrombin time, followed by immunoglobulin M response. The model allowed the threshold determination for each continuous blood or haematological parameter and drew a path toward the outcome. The model’s performance (accuracy, 75.93%; sensitivity, 99.61%; and specificity, 23.43%) was validated with a k-fold repeated cross-validation. The results suggest that a machine learning approach could help clinicians to obtain information that could be useful as an alert for disease progression in patients with COVID-19. Further research should explore the acceptability of these results to physicians in current practice and analyze the impact of machine learning-guided decisions on patient outcomes.

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Section: Discussionmentioning

confidence: 99%

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Giotta

Trerotoli

Palmieri

et al. 2022

IJERPH

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“…Different authors mentioned in a report of the analysis carried out with a database of 3894 patients in Italy [33], obtaining values for a death risk target with an accuracy rating of 83.4%, F1 value of 90.4%, specificity of 30.8%, and Recall of 95.2% when using Random Forest over a dataset with variables such as Glomerular Filtration Rate (eGFR), C-Reactive Protein (CRP), Age, Diabetes, Sex, Hypertension, Smoking, Lung Disease, Myocardial Infection, Obesity, Heart Failure, and Cancer, demonstrating this as vulnerable to those who exhibit the above-mentioned comorbidities. To achieve a comparison between the variables established with the results of this analysis, the scores of the three targets used (HAS, Diabetes, and Obesity) were taken into account and the average was calculated, obtaining values of 84.50% for CA, 83.43% of F1, 84.76% Precision, and 84.53% of Recall.…”

Section: Discussionmentioning

confidence: 99%

Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Cristian¹,

Conte-Galvan²,

Zúñiga

et al. 2021

Information

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Background: The current pandemic caused by SARS-CoV-2 is an acute illness of global concern. SARS-CoV-2 is an infectious disease caused by a recently discovered coronavirus. Most people who get sick from COVID-19 experience either mild, moderate, or severe symptoms. In order to help make quick decisions regarding treatment and isolation needs, it is useful to determine which significant variables indicate infection cases in the population served by the Tijuana General Hospital (Hospital General de Tijuana). An Artificial Intelligence (Machine Learning) mathematical model was developed in order to identify early-stage significant variables in COVID-19 patients. Methods: The individual characteristics of the study subjects included age, gender, age group, symptoms, comorbidities, diagnosis, and outcomes. A mathematical model that uses supervised learning algorithms, allowing the identification of the significant variables that predict the diagnosis of COVID-19 with high precision, was developed. Results: Automatic algorithms were used to analyze the data: for Systolic Arterial Hypertension (SAH), the Logistic Regression algorithm showed results of 91.0% in area under ROC (AUC), 80% accuracy (CA), 80% F1 and 80% Recall, and 80.1% precision for the selected variables, while for Diabetes Mellitus (DM) with the Logistic Regression algorithm it obtained 91.2% AUC, 89.2% accuracy, 88.8% F1, 89.7% precision, and 89.2% recall for the selected variables. The neural network algorithm showed better results for patients with Obesity, obtaining 83.4% AUC, 91.4% accuracy, 89.9% F1, 90.6% precision, and 91.4% recall. Conclusions: Statistical analyses revealed that the significant predictive symptoms in patients with SAH, DM, and Obesity were more substantial in fatigue and myalgias/arthralgias. In contrast, the third dominant symptom in people with SAH and DM was odynophagia.

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“…for performing predictions [ 14 ]. A cohort study with Brazilian data was conducted by authors, and the outcome of the disease in COVID-19-positive patients through Machine Learning was predicted [ 15 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Guleria¹,

Ahmed

Alhumam

et al. 2022

Healthcare

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Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.

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Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data

Cited by 29 publications

References 33 publications

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Application of a Decision Tree Model to Predict the Outcome of Non-Intensive Inpatients Hospitalized for COVID-19

Early Stage Identification of COVID-19 Patients in Mexico Using Machine Learning: A Case Study for the Tijuana General Hospital

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

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