ArticleChaos, solitons, and fractals2020
Comparison of deep learning approaches to predict COVID-19 infection.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 2 of them syntheses that pooled it.
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Who cites it
93 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Immune transcriptomic analysis on COVID-19 patients with varying clinical presentations identifies severity markers.Scientific reports · 2025Article
- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.Scientific reports · 2025Article
- Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer.NPJ precision oncology · 2024Article
- Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.Scientific reports · 2024Article
- Robust AUC optimization under the supervision of clean data.Scientific reports · 2024Article
- Explainable text-tabular models for predicting mortality risk in companion animals.Scientific reports · 2024Article
- Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning.Sensors (Basel, Switzerland) · 2024Review
- Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.JAMIA open · 2024Article
- CoSev: Data-Driven Optimizations for COVID-19 Severity Assessment in Low-Sample Regimes.Diagnostics (Basel, Switzerland) · 2024Article
- Metabolic predictors of COVID-19 mortality and severity: a survival analysis.Frontiers in immunology · 2024Article
- Accuracy of US CDC COVID-19 forecasting models.Frontiers in public health · 2024Article
- Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19.Biophysics reviews · 2023Review
- A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images.Interdisciplinary sciences, computational life sciences · 2023Article
- Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection.Interdisciplinary sciences, computational life sciences · 2023Article
- An innovative ensemble model based on deep learning for predicting COVID-19 infection.Scientific reports · 2023Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.