Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.
Smart Health (Amst)
; 26: 100323, 2022 Dec.
Article
in English
| MEDLINE | ID: covidwho-2086730
ABSTRACT
The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome:
Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.
AUC-ROC, Area under the Receiver-Operating Characteristic curve; COVID-19, Coronavirus disease 2019; Co-occurrence analysis; Epidemiology; ICU, Intensive Care Unit; MCC, Matthew's Correlation Coefficient; ML, Machine learning; Network density; OR, Odds ratio; PCA, Principal Component Analysis; Risk-factors; SARS-CoV-2; SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2; SHAP, Shapley Additive exPlanations; SIVEP-Gripe, Sistema de Informação de Vigilância Epidemiológica da Gripe; SVM, Support Vector Machine; XGBoost, Extreme Gradient Boosting
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
Smart Health (Amst)
Year:
2022
Document Type:
Article
Affiliation country:
J.smhl.2022.100323
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