ArticleBMC infectious diseases2023
Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.
Article in BMC infectious diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- High nasopharyngeal and serum IL-6 levels and the - 573G > C polymorphism (rs1800796) are linked with the risk of severe COVID-19 in a Mexican population: a case‒control study.BMC infectious diseases · 2025Article
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.Frontiers in artificial intelligence · 2024Review
- Blood ACE2 Protein Level Correlates with COVID-19 Severity.International journal of molecular sciences · 2023Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group.
methodsA total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers.
resultsModels based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes.
conclusionsML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.
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