ArticleFrontiers in public health2021
Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.Computational biology and chemistry · 2022Pooled it
- Leakage-Controlled and Survey-Weighted Machine Learning for Neonatal Mortality Risk Prediction Using NFHS-5 Data.Healthcare (Basel, Switzerland) · 2026Article
- Wave-aware mortality prediction in COVID-19: a multi-stage feature selection and explainable machine-learning framework.BMC medical informatics and decision making · 2026Article
- Generative Artificial Intelligence in Health Informatics Education: A Qualitative Exploration of Student Engagement, Perceived Educational Implications, and Ethical Considerations.Advances in medical education and practice · 2026Article
- Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health.BMC public health · 2025Article
- Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.BMC medical informatics and decision making · 2025Article
- Data Mining of Infertility and Factors Influencing Its Development: A Finding From a Prospective Cohort Study of RaNCD in Iran.Health science reports · 2025Article
- Data-driven rapid detection ofHeliyon · 2024Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Explainable machine learning predicts survival of retroperitoneal liposarcoma: A study based on the SEER database and external validation in China.Cancer medicine · 2024Article
- Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation.Infectious Disease Modelling · 2024Article
- Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Observational
- Prediction of Surgical Approach in Mitral Valve Disease by XGBoost Algorithm Based on Echocardiographic Features.Journal of clinical medicine · 2023Article
- An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers.Antioxidants (Basel, Switzerland) · 2023Article
- Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.BMC infectious diseases · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner.
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