Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

Passarelli‐Araujo, Hemanoel; Passarelli-Araujo, Hisrael; Urbano, Mariana Ragassi; Pescim, Rodrigo R.

doi:10.1016/j.smhl.2022.100323

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…These findings aligned with previous studies regarding clinical features and the frequency of comorbidities in patients with COVID-19. Consistent with previous reports, advanced age emerged as the most significant predictor of severe outcomes ( 48 , 55 , 59 – 63 ). Male sex was identified as a high-risk factor in in-patients with COVID-19 ( 15 , 16 , 24 , 59 – 62 , 64 ).…”

Section: Discussionsupporting

confidence: 91%

“…Beeswarm plot ( Figure 4A ) ( 54 , 55 ), indicated the range across the SHAP value and pointed out the degradation probability, expressed as the logarithm of the odds ( 56 ). We could get a general idea of the directional impact of the features in relation to the distribution of “red” and “blue” dots.…”

Section: Model Interpretationmentioning

confidence: 99%

“…A position of the point along the x -axis (i.e., SHAP value) represents the features’ influence on the model output for the explicit patient. A higher SHAP value has a higher impact on “mortality” relative to a lower SHAP value ( 54 , 55 ). The features are arranged in order of importance on the y -axis, i.e., the more important features are placed at the top, given by their absolute Shapley values.…”

Section: Model Interpretationmentioning

confidence: 99%

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Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

Datta

Dalmida

Martinez

et al. 2023

Front. Digit. Health

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IntroductionThe SARS-CoV-2 (COVID-19) pandemic has created substantial health and economic burdens in the US and worldwide. As new variants continuously emerge, predicting critical clinical events in the context of relevant individual risks is a promising option for reducing the overall burden of COVID-19. This study aims to train an AI-driven decision support system that helps build a model to understand the most important features that predict the “mortality” of patients hospitalized with COVID-19.MethodsWe conducted a retrospective analysis of “5,371” patients hospitalized for COVID-19-related symptoms from the South Florida Memorial Health Care System between March 14th, 2020, and January 16th, 2021. A data set comprising patients’ sociodemographic characteristics, pre-existing health information, and medication was analyzed. We trained Random Forest classifier to predict “mortality” for patients hospitalized with COVID-19.ResultsBased on the interpretability of the model, age emerged as the primary predictor of “mortality”, followed by diarrhea, diabetes, hypertension, BMI, early stages of kidney disease, smoking status, sex, pneumonia, and race in descending order of importance. Notably, individuals aged over 65 years (referred to as “older adults”), males, Whites, Hispanics, and current smokers were identified as being at higher risk of death. Additionally, BMI, specifically in the overweight and obese categories, significantly predicted “mortality”. These findings indicated that the model effectively learned from various categories, such as patients' sociodemographic characteristics, pre-hospital comorbidities, and medications, with a predominant focus on characterizing pre-hospital comorbidities. Consequently, the model demonstrated the ability to predict “mortality” with transparency and reliability.ConclusionAI can potentially provide healthcare workers with the ability to stratify patients and streamline optimal care solutions when time is of the essence and resources are limited. This work sets the platform for future work that forecasts patient responses to treatments at various levels of disease severity and assesses health disparities and patient conditions that promote improved health care in a broader context. This study contributed to one of the first predictive analyses applying AI/ML techniques to COVID-19 data using a vast sample from South Florida.

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Section: Discussionsupporting

confidence: 91%

Section: Model Interpretationmentioning

confidence: 99%

Section: Model Interpretationmentioning

confidence: 99%

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Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

Datta

Dalmida

Martinez

et al. 2023

Front. Digit. Health

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“…Finalmente, Passarelli-Araujo [18] realizaram um estudo retrospectivo da importância das variáveis demográficas e clínicas na mortalidade por COVID-19 e como as redes de comorbidades são estruturadas conforme as faixas etárias em pacientes hospitalizados em Londrina, Paraná, Brasil, cadastrados no SIVEP-Gripe, nas datas de janeiro de 2021 a fevereiro de 2022. Foram utilizados os algoritmos de Regressão Logística, Support Vector Machine (SVM), Random Forest e XGBoost de aprendizado de máquina para prever o resultado da COVID-19.…”

Section: Trabalhos Relacionadosunclassified

Análise de Desfechos de COVID-19 no RJ através de Técnicas de Aprendizado de Máquina

Zavaleta,

Silva,

Oliveira

et al. 2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Given the rapid spread of COVID-19, having tools to screen patients and reduce the risk of death is crucial. This study focuses on the outcomes (cures and deaths) of confirmed COVID-19 cases in Rio de Janeiro State for both vaccinated and unvaccinated patients. Machine Learning (ML) algorithms were used to classify outcomes based on symptom, comorbidity, and age data obtained from the State Health Secretariat of Rio de Janeiro. After cleaning the dataset and selecting relevant attributes, the final model achieved an accuracy of 87,3% and a precision of 86,6% in predicting outcomes for unvaccinated patients. Similarly, the final model for vaccinated patients achieved an accuracy of 86,3% and a precision of 83,1% in predicting outcomes. In addition, the attributes of patients that stand out with and without the vaccine were evaluated. Overall, these results demonstrate the potential benefits of using machine learning methods to improve patient screening and reduce the risk of COVID-19-related deaths.

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“…Machine learning is crucial in today's environment, especially in healthcare. Machine learning techniques are being used to help with hospital system restructuring, infectious disease detection and treatment, and medical treatment [10][11][12][13]. In addition, machine learning models may be used to give intelligent solutions for analyzing vast amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

2022

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Introduction Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors. Materials and methods Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES). Results Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively. Conclusion This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.

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Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

Cited by 10 publications

References 32 publications

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

Análise de Desfechos de COVID-19 no RJ através de Técnicas de Aprendizado de Máquina

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

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