Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico

Guzmán-Torres, José Alberto; Alonso-Guzmán, Elia Mercedes; Domínguez-Mota, Francisco J.; Tinoco-Guerrero, Gerardo

doi:10.1016/j.rinp.2021.104483

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Fidan et al [84] employed clustering techniques to determine cities with similar risk levels, analyzing the incidence of cases and environmental parameters. Guzmán-Torres et al [194] pointed out that conditions in each country may differ depending on different factors such as the general health status of the people, reporting that the main causes of death in Mexico are related to age, poor eating habits, chronic diseases and contact with infected people who do not have adequate care. Zawbaa et al [82] proposed a comparison of the spread of the disease among nine different countries, revealing that average young age, hot climate, prevalence of Bacillus Calmette-Guérin (BCG) vaccine and malaria treatment are crucial elements decreasing the mortality impact of the virus.…”

Section: Mortality Predictionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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Background: The declaration of the COVID-19 pandemic triggered global efforts to control and manage the virus impact. Scientists and researchers have been strongly involved in developing effective strategies that can help policy makers and healthcare systems both to monitor the spread and to mitigate the impact of the COVID-19 pandemic. Machine Learning (ML) and Artificial Intelligence (AI) have been applied in several fronts of the fight. Foremost is diagnostic assistance, encompassing patient triage, prediction of ICU admission and mortality, identification of mortality risk factors, and discovering treatment drugs and vaccines. Objective: This systematic review aims to identify original research studies involving actual patient data to construct ML- and AI-based models for clinical decision support for early response during the pandemic years. Methods: Following the PRISMA methodology, two large academic research publication indexing databases were searched to investigate the use of ML-based technologies and their applications in healthcare to combat the COVID-19 pandemic. Results: The literature search returned more than 1000 papers; 220 were selected according to specific criteria. The selected studies illustrate the usefulness of ML with respect to supporting healthcare professionals for (1) triage of patients depending on disease severity, (2) predicting admission to hospital or Intensive Care Units (ICUs), (3) search for new or repurposed treatments and (4) the identification of mortality risk factors. Conclusion: The ML/AI research community was able to propose and develop a wide variety of solutions for predicting mortality, hospitalizations and treatment recommendations for patients with COVID-19 diagnostic, opening the door for further integration of ML in clinical practices fighting this and forecoming pandemics. However, the translation to the clinical practice is impeded by the heterogeneity of both the datasets and the methodological and computational approaches. The literature lacks robust model validations supporting this desired translation.

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Section: Mortality Predictionmentioning

confidence: 99%

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Badiola-Zabala,

Lopez-Guede,

Estevez

et al. 2024

Electronics

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“…worldwide. As a result, multiple papers have been published regarding this issue [5,6,[22][23][24][25][26][27]. In addition, a significant amount of research has been put forward using artificial intelligence on COVID-19 data.…”

Section: Data Mining On Covid-19 Datamentioning

confidence: 99%

“…In the studies [5,[22][23][24] Decision Trees were the best algorithm in the prediction models with accuracies of 99.85%, 94.99%, 98.7%, and 99.98%, respectively. In [6,[25][26][27], different algorithms such as K-Nearest Neighbor, Logistic Regression, Bayesian Network, and Random Forest proved to be the best in prediction models with accuracies of 98%, 87%, 89.31%, and 90.83%, respectively. These studies were beneficial for combat against COVID-19, with the accuracies reaching high values-between 99.98% [24] and 87% [25].…”

Section: Data Mining On Covid-19 Datamentioning

confidence: 99%

“…In [6,[25][26][27], different algorithms such as K-Nearest Neighbor, Logistic Regression, Bayesian Network, and Random Forest proved to be the best in prediction models with accuracies of 98%, 87%, 89.31%, and 90.83%, respectively. These studies were beneficial for combat against COVID-19, with the accuracies reaching high values-between 99.98% [24] and 87% [25]. The good results from the different models show that the development of exact prediction models can be a good investment to help fight against pandemics.…”

Section: Data Mining On Covid-19 Datamentioning

confidence: 99%

“…Orange Data Mining provides a vast Data Mining algorithm's range. Naïve Bayes, Decision Tree, Random Forest, and Logistic Regression were used in this study as state-ofthe-art [5,6,[22][23][24][25][26][27]. The following subsections briefly describe each of these algorithms.…”

Section: Algorithmsmentioning

confidence: 99%

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Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data

2022

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COVID-19 has provoked enormous negative impacts on human lives and the world economy. In order to help in the fight against this pandemic, this study evaluates different databases’ systems and selects the most suitable for storing, handling, and mining COVID-19 data. We evaluate different SQL and NoSQL database systems using the following metrics: query runtime, memory used, CPU used, and storage size. The databases systems assessed were Microsoft SQL Server, MongoDB, and Cassandra. We also evaluate Data Mining algorithms, including Decision Trees, Random Forest, Naive Bayes, and Logistic Regression using Orange Data Mining software data classification tests. Classification tests were performed using cross-validation in a table with about 3 M records, including COVID-19 exams with patients’ symptoms. The Random Forest algorithm has obtained the best average accuracy, recall, precision, and F1 Score in the COVID-19 predictive model performed in the mining stage. In performance evaluation, MongoDB has presented the best results for almost all tests with a large data volume.

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Analysis of an ongoing epidemic: Advantages and limitations of COVID-19 modeling

Santana-Cibrian¹,

Acuña-Zegarra²,

Tocto-Erazo³

et al. 2023

Mathematical Modelling, Simulations, and AI for Emergent Pandemic Diseases

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Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico

Cited by 7 publications

References 17 publications

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022

Assessment of SQL and NoSQL Systems to Store and Mine COVID-19 Data

Analysis of an ongoing epidemic: Advantages and limitations of COVID-19 modeling

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