ArticleJournal of the American Medical Informatics Association : JAMIA2021
Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. 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.
- Identifying and understanding significant change due to drift when assessing AI models in healthcare: a narrative review.BMJ digital health & AI · 2026Review
- Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.JMIR medical informatics · 2025Article
- Multisource Coherence Analysis of the First European Multicenter Cohort Study for Cancer Prevention in People Experiencing Homelessness: Data Quality Study.JMIR medical informatics · 2025Article
- Multi-Modal Machine Learning Approach for COVID-19 Detection Using Biomarkers and X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Resilient Artificial Intelligence in Health: Synthesis and Research Agenda Toward Next-Generation Trustworthy Clinical Decision Support.Journal of medical Internet research · 2024Review
- Genomic, Proteomic, and Phenotypic Biomarkers of COVID-19 Severity: Protocol for a Retrospective Observational Study.JMIR research protocols · 2024Article
- Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.IEEE open journal of engineering in medicine and biology · 2024Article
- Which decisions affect cohort distribution in COVID-19 data analytics?AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Quantum computing and machine learning for Arabic language sentiment classification in social media.Scientific reports · 2023Article
- Predictive models of long COVID.EBioMedicine · 2023Article
- Data drift in medical machine learning: implications and potential remedies.The British journal of radiology · 2023Review
- Predicting COVID-19 pandemic waves including vaccination data with deep learning.Frontiers in public health · 2023Article
- Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application.Sensors (Basel, Switzerland) · 2022Article
- Bias in algorithms of AI systems developed for COVID-19: A scoping review.Journal of bioethical inquiry · 2022Article
- Building Process-Oriented Data Science Solutions for Real-World Healthcare.International journal of environmental research and public health · 2022Article
- An analysis of the accuracy of COVID-19 country transmission classification.Scientific reports · 2022Article
- Subphenotyping of Mexican Patients With COVID-19 at Preadmission To Anticipate Severity Stratification: Age-Sex Unbiased Meta-Clustering Technique.JMIR public health and surveillance · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- County-level societal predictors of COVID-19 cases and deaths changed through time in the United States: A longitudinal ecological study.PLOS global public health · 2022Article
- COVID-19 outbreaks analysis in the Valencian Region of Spain in the prelude of the third wave.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning. MATERIALS AND
methodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.
resultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.
conclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.
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