COVID-19 diagnosis by routine blood tests using machine learning

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Scientific and ethical basis for social-distancing interventions against COVID-19

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Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections.

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Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study

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Potential preanalytical and analytical vulnerabilities in the laboratory diagnosis of coronavirus disease 2019 (COVID-19)

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K-Sample Anderson-Darling Tests

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False-Negative Results of Real-Time Reverse-Transcriptase Polymerase Chain Reaction for Severe Acute Respiratory Syndrome Coronavirus 2: Role of Deep-Learning-Based CT Diagnosis and Insights from Two Cases

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An application of machine learning to haematological diagnosis

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An alternative SMOTE oversampling strategy for high-dimensional datasets

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SARS‐CoV‐2, bacterial co‐infections, and AMR: the deadly trio in COVID‐19?

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Overview: Systemic Inflammatory Response Derived From Lung Injury Caused by SARS-CoV-2 Infection Explains Severe Outcomes in COVID-19

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COVID-19 diagnosis prediction in emergency care patients: a machine learning approachPreprint

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A role for CT in COVID-19? What data really tell us so far

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Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test resultsPreprint

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openTSNE: a modular Python library for t-SNE dimensionality reduction and embeddingPreprint

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Early evidence of effectiveness of digital contact tracing for SARS-CoV-2 in Switzerland

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Diagnosing brain tumours by routine blood tests using machine learning

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A Regularization-Based eXtreme Gradient Boosting Approach in Foodborne Disease Trend Forecasting

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Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

Alballa, N., & Al-Turaiki, I. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100564 · Cited by 226 · see citing works

Applications of artificial intelligence in battling against covid-19: A literature review

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Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests

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Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms

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Ensemble learning model for diagnosing COVID-19 from routine blood tests

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Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

Nwanosike, E. M., Conway, B. R., Merchant, H. A., & Hasan, S. S. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104679 · Cited by 140 · see citing works

Explaining machine learning based diagnosis of COVID-19 from routine blood tests with decision trees and criteria graphs

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Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

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Automatic COVID-19 prediction using explainable machine learning techniques

Solayman, S., Aumi, S. A., Mery, C. S., Mubassir, M., & Khan, R. (2023) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2023.01.003 · Cited by 82 · see citing works

Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time

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COVID-19 Detection Empowered with Machine Learning and Deep Learning Techniques: A Systematic Review

Rehman, A., Iqbal, M. A., Xing, H., & Ahmed, I. (2021) · Applied Sciences · doi:10.3390/app11083414 · Cited by 68 · see citing works

Accurate detection of Covid-19 patients based on Feature Correlated Naïve Bayes (FCNB) classification strategy

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Application of artificial intelligence in COVID-19 medical area: a systematic review

Chang, Z., Zhan, Z., Zhao, Z., You, Z., Liu, Y., Yan, Z., Fu, Y., Liang, W., & Zhao, L. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-747 · Cited by 59 · see citing works

Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

Chadaga, K., Chakraborty, C., Prabhu, S., Umakanth, S., Vivekananda Bhat, K., & Sampathila, N. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-021-00499-4 · Cited by 58 · see citing works

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami, M., & Oussalah, M. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100941 · Cited by 58 · see citing works

Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening

Soltan, A. A. S., Yang, J., Pattanshetty, R., Novak, A., Yang, Y., Rohanian, O., Beer, S., Soltan, M. A., Thickett, D. R., Fairhead, R., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00272-7 · Cited by 54 · see citing works

COVID-19 diagnosis from routine blood tests using artificial intelligence techniques

Rikan, S. B., Azar, A. S., Ghafari, A., Bagherzadeh, J., & Pirnejad, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103263 · Cited by 50 · see citing works

NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms

Soui, M., Mansouri, N., Alhamad, R. I., Kessentini, M., & Ghédira, K. (2021) · Nonlinear Dynamics · doi:10.1007/s11071-021-06504-1 · Cited by 50 · see citing works

Diagnosing COVID-19 using artificial intelligence: a comprehensive review

Khanna, V. V., Chadaga, K., Sampathila, N., Prabhu, S., Chadaga, R., & Umakanth, S. (2022) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-022-00367-1 · Cited by 48 · see citing works

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application

Velichko, A., Huyut, M. T., Belyaev, M. A., Izotov, Y., & Korzun, D. G. (2022) · Sensors · doi:10.3390/s22207886 · Cited by 48 · see citing works

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Abayomi‐Alli, O., Damaševičius, R., Maskeliūnas, R., & Misra, S. (2022) · Sensors · doi:10.3390/s22062224 · Cited by 47 · see citing works

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

Baddal, B., Taner, F., & Ozsahin, D. U. (2024) · Diagnostics · doi:10.3390/diagnostics14050484 · Cited by 47 · see citing works

Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis

Cheah, B. C. J., Vicente, C. R., & Chan, K. R. (2025) · Viruses · doi:10.3390/v17070882 · Cited by 43 · see citing works

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Huyut, M. T., & Velichko, A. (2022) · Sensors · doi:10.3390/s22134820 · Cited by 41 · see citing works

Analyzing the Effect of Filtering and Feature-Extraction Techniques in a Machine Learning Model for Identification of Infectious Disease Using Radiography Imaging

Rasheed, J. (2022) · Symmetry · doi:10.3390/sym14071398 · Cited by 39 · see citing works

An integrated analysis and comparison of serum, saliva and sebum for COVID-19 metabolomics

Spick, M. P., Lewis, H.-M., Frampas, C., Longman, K., Costa, C., Stewart, A., Dunn‐Walters, D. K., Greener, D., Evetts, G., Wilde, M. J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-16123-4 · Cited by 39 · see citing works

Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives

Paul, S. G., Saha, A., Biswas, A. A., Zulfiker, M. S., Arefin, M. S., Rahman, M. M., & Reza, A. W. (2022) · Array · doi:10.1016/j.array.2022.100271 · Cited by 38 · see citing works

Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests

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The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection

Lee, Y., Kim, Y.-S., Lee, D., Jeong, S., Kang, G.-H., Jang, Y. S., Kim, W., Choi, H. Y., Kim, J. G., & Choi, S.-H. (2022) · Scientific Reports · doi:10.1038/s41598-022-05069-2 · Cited by 35 · see citing works

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

Song, X., Zhu, J., Tan, X., Yu, W., Wang, Q., Shen, D., & Chen, W. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.926069 · Cited by 35 · see citing works

An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works

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Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases

Alqaissi, E., Alotaibi, F. S., & Ramzan, M. S. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/6902321 · Cited by 34 · see citing works

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees

Huyut, M. T., & Huyut, Z. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14015 · Cited by 34 · see citing works

Potential of Microneedle Systems for COVID-19 Vaccination: Current Trends and Challenges

Hassan, J., Haigh, C., Ahmed, T., Uddin, M. J., & Das, D. B. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14051066 · Cited by 34 · see citing works

Predictive models for COVID-19 detection using routine blood tests and machine learning

Kistenev, Y. V., Vrazhnov, D. A., Shnaider, E. E., & Zuhayri, H. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11185 · Cited by 33 · see citing works

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph

Du, R., Tsougenis, E., Ho, J. W. K., Chan, J. K. Y., Chiu, K. W.-H., Fang, B., Ng, M., Leung, S. T., Lo, C. S.-Y., Wong, H. Y. F., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-93719-2 · Cited by 29 · see citing works

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count

Campagner, A., Carobene, A., & Cabitza, F. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00167-3 · Cited by 29 · see citing works

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Kouanou, A. T., Attia, T. M., Feudjio, C., Djeumo, A. F., Mouelas, A. N., Nzogang, M. P., Tchapga, C. T., & Tchiotsop, D. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/4733167 · Cited by 24 · see citing works

Detection of Risk Predictors of COVID-19 Mortality with Classifier Machine Learning Models Operated with Routine Laboratory Biomarkers

Huyut, M. T., Velichko, A., & Belyaev, M. A. (2022) · Applied Sciences · doi:10.3390/app122312180 · Cited by 28 · see citing works

Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning

Cobre, A. D. F., Surek, M., Stremel, D. P., Fachi, M. M., Borba, H. H. L., Tonin, F. S., & Pontarolo, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105659 · Cited by 25 · see citing works

Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: Serum levels of zinc, calcium, and vitamin D

Jahangirimehr, A., Shahvali, E. A., Rezaeijo, S. M., Khalighi, A., Honarmandpour, A., Honarmandpour, F., Labibzadeh, M., Bahmanyari, N., & Heydarheydari, S. (2022) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2022.07.011 · Cited by 24 · see citing works

SARS-Cov2 enables anaerobic bacteria (Prevotella, et al) to colonize the lungs disrupting homeostasis, causing long-drawn chronic symptoms, and acute severe symptoms (ARDS, septic shock, clots, arterial stroke) which finds resonance, with key differences, in the ‘forgotten disease’ Lemierre Syndrome, enabled by Epstein Barr VirusPreprint

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Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study

Dipaola, F., Gatti, M., Levra, A. G., Menè, R., Shiffer, D., Faccincani, R., Raouf, Z., Secchi, A., Rovere, P. Q., Voza, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-37512-3 · Cited by 22 · see citing works

Classification of COVID-19 by using supervised optimized machine learning technique

Sharma, D. K., Subramanian, M., Malyadri, P., Reddy, B. S., Sharma, M., & Tahreem, M. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.11.388 · Cited by 22 · see citing works

MALDI-TOF mass spectrometry of saliva samples as a prognostic tool for COVID-19

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Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

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COVID-19 and Artificial Intelligence: An Approach to Forecast the Severity of Diagnosis

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Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19

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Speech as a Biomarker for COVID-19 Detection Using Machine Learning

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Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review

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Black Fungus Infection Detection using AI-based Early Warning System for Patients through Multi-Modal Medical Imaging

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Towards Applicability of Information Communication Technologies in Automated Disease Detection

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Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning

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Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning

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ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis

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Artificial intelligence enabled COVID-19 detection: techniques, challenges and use cases

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An Explainable System for Diagnosis and Prognosis of COVID-19

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Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

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Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study

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A Novel Method for Prediction and Analysis of COVID 19 Transmission Using Machine Learning Based Time Series Models

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Neuromorphic chips for biomedical engineering

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A Scoping Literature Review of Artificial Intelligence in Epidemiology: Uses, Applications, Challenges and Future Trends

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Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

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Using Artificial Intelligence for COVID-19 Detection in Blood Exams: A Comparative Analysis

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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study

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A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

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SARS-CoV-2 amyloid, is COVID-19-exacerbated dementia an amyloid disorder in the making?

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COVIDAL: A Machine Learning Classifier for Digital COVID-19 Diagnosis in German Hospitals

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Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data

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Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset

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Supporting Clinical COVID-19 Diagnosis with Routine Blood Tests Using Tree-Based Entropy Structured Self-Organizing Maps

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Serum N-glycomic profiling may provide potential signatures for surveillance of COVID-19

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Accurate detection of coronavirus cases using deep learning with attention mechanism and genetic algorithm

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Proof of concept of the potential of a machine learning algorithm to extract new information from conventional SARS-CoV-2 rRT-PCR results

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A methodological study for the diagnosis of the SARS-Cov-2 infection in human serum with a macrocyclic sensor array

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COVID-19 Outbreak Prediction Analysis using Machine Learning

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Artificial Intelligence-Based Predictive Tools for Life-Threatening Diseases

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Development of Risk-Free COVID-19 Screening Algorithm from Routine Blood Test using Ensemble Machine Learning.Preprint

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Machine Learning Models to Discriminate COVID-19 Severity with Biomarkers Available in Brazilian Public Health

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Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study

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Clinical and Laboratory Approach to Diagnose COVID-19 Using Machine Learning

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MACHINE LEARNING-BASED COVID-19 PREDICTION FROM BLOOD TEST PARAMETERS

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Detection of the Most Essential Characteristics from Blood Routine Tests to Increase COVID-19 Diagnostic Capacity by Using Machine Learning Algorithms

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A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques

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A machine learning tool for the diagnosis of SARS‐CoV ‐2 infection from hemogram parameters

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