ArticleJournal of medical systems2022
Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.
Article in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Multi-class anatomical landmark detection in periapical radiographs with deep learning.Odontology · 2026Article
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- Machine learning based prediction of platelet concentration from ROTEM measurements.Scientific reports · 2026Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.Scientific reports · 2025Article
- A multi-modal graph-based framework for Alzheimer's disease detection.Scientific reports · 2025Article
- One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.Scientific reports · 2025Article
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms.Scientific reports · 2023Article
- Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Data drift in medical machine learning: implications and potential remedies.The British journal of radiology · 2023Review
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- A Decision Support System for Diagnosis of COVID-19 from Non-COVID-19 Influenza-like Illness Using Explainable Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2023Article
- Diagnostic ability of macular microvasculature with swept-source OCT angiography for highly myopic glaucoma using deep learning.Scientific reports · 2023Article
- Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study.Translational vision science & technology · 2022Article
- Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities.JMIR medical informatics · 2022Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
- Analyzing Patient Trajectories With Artificial Intelligence.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Many previous studies claim to have developed machine learning models that diagnose COVID-19 from blood tests. However, we hypothesize that changes in the underlying distribution of the data, so called domain shifts, affect the predictive performance and reliability and are a reason for the failure of such machine learning models in clinical application. Domain shifts can be caused, e.g., by changes in the disease prevalence (spreading or tested population), by refined RT-PCR testing procedures (way of taking samples, laboratory procedures), or by virus mutations. Therefore, machine learning models for diagnosing COVID-19 or other diseases may not be reliable and degrade in performance over time. We investigate whether domain shifts are present in COVID-19 datasets and how they affect machine learning methods. We further set out to estimate the mortality risk based on routinely acquired blood tests in a hospital setting throughout pandemics and under domain shifts. We reveal domain shifts by evaluating the models on a large-scale dataset with different assessment strategies, such as temporal validation. We present the novel finding that domain shifts strongly affect machine learning models for COVID-19 diagnosis and deteriorate their predictive performance and credibility. Therefore, frequent re-training and re-assessment are indispensable for robust models enabling clinical utility.
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Registered trials
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