ArticlePloS one2021
Identification of high-risk COVID-19 patients using machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- AI-RiskX: An Explainable Deep Learning Approach for Identifying At-Risk Patients During Pandemics.Bioengineering (Basel, Switzerland) · 2025Article
- Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.Scientific reports · 2025Article
- A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing.ACS sensors · 2025Review
- Article
- A systematic review of Machine Learning and Deep Learning approaches in Mexico: challenges and opportunities.Frontiers in artificial intelligence · 2024Review
- Machine Learning with Human Resources Data: Predicting Turnover among Community Mental Health Center Employees.The journal of mental health policy and economics · 2023Article
- DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.Bioengineering (Basel, Switzerland) · 2023Article
- Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.Health science reports · 2023Article
- COVID-19 detection based on self-supervised transfer learning using chest X-ray images.International journal of computer assisted radiology and surgery · 2023Article
- Classification of COVID-19 Patients into Clinically Relevant Subsets by a Novel Machine Learning Pipeline Using Transcriptomic Features.International journal of molecular sciences · 2023Article
- Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants.Vaccines · 2023Review
- Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study.Frontiers in medicine · 2023Article
- Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images.Multimedia tools and applications · 2023Article
- An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works.Multimedia systems · 2023Article
- Prognostic Model of COVID-19 Severity and Survival among Hospitalized Patients Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical Profiles at the Time of Diagnosis of SARS-CoV-2 Infection in Costa Rica During the Pre-vaccination Period Using a Machine Learning Approach.Phenomics (Cham, Switzerland) · 2022Article
- Identifying the High-Risk Population for COVID-19 Transmission in Hong Kong Leveraging Explainable Machine Learning.Healthcare (Basel, Switzerland) · 2022Article
- Hematological- and Immunological-Related Biomarkers to Characterize Patients with COVID-19 from Other Viral Respiratory Diseases.Journal of clinical medicine · 2022Article
- A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- Long-COVID diagnosis: From diagnostic to advanced AI-driven models.European journal of radiology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current COVID-19 public health crisis, caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has produced a devastating toll both in terms of human life loss and economic disruption. In this paper we present a machine-learning algorithm capable of identifying whether a given patient (actually infected or suspected to be infected) is more likely to survive than to die, or vice-versa. We train this algorithm with historical data, including medical history, demographic data, as well as COVID-19-related information. This is extracted from a database of confirmed and suspected COVID-19 infections in Mexico, constituting the official COVID-19 data compiled and made publicly available by the Mexican Federal Government. We demonstrate that the proposed method can detect high-risk patients with high accuracy, in each of four identified clinical stages, thus improving hospital capacity planning and timely treatment. Furthermore, we show that our method can be extended to provide optimal estimators for hypothesis-testing techniques commonly-used in biological and medical statistics. We believe that our work could be of use in the context of the current pandemic in assisting medical professionals with real-time assessments so as to determine health care priorities.
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