ArticleBioengineering (Basel, Switzerland)2023
A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- A stacked ensemble model with NNLS-based weighting for influenza forecasting: a case study of Anhui Province, China.Frontiers in public health · 2026Article
- Explainability in action: A metric-driven assessment of local explanations for healthcare tabular models.PloS one · 2026Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.BMC infectious diseases · 2025Article
- Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images.Scientific reports · 2024Article
- ExplainableJournal of cancer research and clinical oncology · 2024Article
- Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization.Sensors (Basel, Switzerland) · 2024Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Artificial intelligence for diagnosis of mild-moderate COVID-19 using haematological markers.Annals of medicine · 2023Article
- DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy.Medicina (Kaunas, Lithuania) · 2023Article
- Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Three Logistic Predictive Models for the Prediction of Mortality and Major Pulmonary Complications after Cardiac Surgery.Medicina (Kaunas, Lithuania) · 2023Observational
- Brixia Chest X-ray Score, Laboratory Parameters and Vaccination Status for Prediction of Mortality in COVID-19 Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.Bioengineering (Basel, Switzerland) · 2023Article
- The COVID-19 Pandemic: How Technology Is Reshaping Public Health and Medicine.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast number of people all around the world. Fortunately, vaccines have been discovered, and they seem effectual in controlling the severe prognosis induced by the virus. The reverse transcription-polymerase chain reaction (RT-PCR) test is the current golden standard for diagnosing different infectious diseases, including COVID-19; however, it is not always accurate. Therefore, it is extremely crucial to find an alternative diagnosis method which can support the results of the standard RT-PCR test. Hence, a decision support system has been proposed in this study that uses machine learning and deep learning techniques to predict the COVID-19 diagnosis of a patient using clinical, demographic and blood markers. The patient data used in this research were collected from two Manipal hospitals in India and a custom-made, stacked, multi-level ensemble classifier has been used to predict the COVID-19 diagnosis. Deep learning techniques such as deep neural networks (DNN) and one-dimensional convolutional networks (1D-CNN) have also been utilized. Further, explainable artificial techniques (XAI) such as Shapley additive values (SHAP), ELI5, local interpretable model explainer (LIME), and QLattice have been used to make the models more precise and understandable. Among all of the algorithms, the multi-level stacked model obtained an excellent accuracy of 96%. The precision, recall, f1-score and AUC obtained were 94%, 95%, 94% and 98% respectively. The models can be used as a decision support system for the initial screening of coronavirus patients and can also help ease the existing burden on medical infrastructure.
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Registered trials
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.