ArticleScientific reports2024
Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A survey of explainable AI applications in infectious disease diagnosis.BMC infectious diseases · 2026Review
- Immunological Mechanisms and Machine Learning Applications in Post-COVID-19 Syndrome: A Narrative Review.Microorganisms · 2026Article
- Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure.Frontiers in medicine · 2026Article
- Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.Communications medicine · 2025Article
- Explained Deep Learning Framework for COVID-19 Detection in Volumetric CT Images Aligned with the British Society of Thoracic Imaging Reporting Guidance: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- AI-driven prediction of severe respiratory sequelae in COVID-19 patients.Annals of medicine · 2025Article
- Interpretable Machine Learning Model for Predicting and Assessing the Risk of Diabetic Nephropathy: Prediction Model Study.JMIR medical informatics · 2025Article
- AI-RiskX: An Explainable Deep Learning Approach for Identifying At-Risk Patients During Pandemics.Bioengineering (Basel, Switzerland) · 2025Article
- Utilizing artificial intelligence as an arbitrary tool in managing difficult COVID-19 cases in critical care medicine.World journal of critical care medicine · 2025Review
- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Digital Biomarkers and AI for Remote Monitoring of Fatigue Progression in Neurological Disorders: Bridging Mechanisms to Clinical Applications.Brain sciences · 2025Article
- Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants.Scientific reports · 2025Article
- A Machine Learning Model for Predicting Intensive Care Unit Admission in Inpatients with COVID-19 Using Clinical Data and Laboratory Biomarkers.Biomedicines · 2025Article
- Drug utilisation patterns & clinical outcomes in hospitalised COVID-19 patients: A geospatial & machine learning approach.The Indian journal of medical research · 2025Article
- Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.Scientific reports · 2025Article
- Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm.Scientific reports · 2025Article
- Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.Scientific reports · 2025Article
- Machine Learning Models for Differentiating Causes of Dyspnea in Emergency Department Patients: A Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.
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