ArticleGigaScience2022
Statistical quantification of confounding bias in machine learning models.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.Biological psychiatry · 2023Pooled it
- Disentangling EEG Fingerprinting and Sleep Biomarkers Using Generalized Weighted Ordinal Patterns.Brain sciences · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Distinct cognitive and functional connectivity features from healthy cohorts can identify clinical obsessive-compulsive disorder.Molecular psychiatry · 2026Article
- Can we predict sleep health based on brain features? A large-scale machine learning study using the UK Biobank.Brain communications · 2026Article
- ADNEX-AI: automated extraction of ultrasound predictors for interpretable ovarian cancer risk stratification.NPJ precision oncology · 2025Article
- A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology.Renal failure · 2025Article
- On the replicability of diffusion weighted MRI-based brain-behavior models.Communications biology · 2025Article
- Pitfalls in using ML to predict cognitive function performance.Scientific reports · 2025Article
- Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease.Journal of translational medicine · 2025Article
- Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Continuous Monitoring Enables Dynamic Biomarkers to Assess Resilience in Acute COVID-19 Patients.Journal of clinical medicine · 2025Article
- External validation of machine learning models-registered models and adaptive sample splitting.GigaScience · 2025Article
- Distinguishing deception from its confounds by improving the validity of fMRI-based neural prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Decoding Radiomics: A Step-by-Step Guide to Machine Learning Workflow in Hand-Crafted and Deep Learning Radiomics Studies.Diagnostics (Basel, Switzerland) · 2024Review
- An externally validated resting-state brain connectivity signature of pain-related learning.Communications biology · 2024Article
- The effects of data leakage on connectome-based machine learning models.bioRxiv : the preprint server for biology · 2023Article
- Predictive modeling of optimism bias using gray matter cortical thickness.Scientific reports · 2023Article
- Confound-leakage: confound removal in machine learning leads to leakage.GigaScience · 2022Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
backgroundThe lack of nonparametric statistical tests for confounding bias significantly hampers the development of robust, valid, and generalizable predictive models in many fields of research. Here I propose the partial confounder test, which, for a given confounder variable, probes the null hypotheses of the model being unconfounded.
resultsThe test provides a strict control for type I errors and high statistical power, even for nonnormally and nonlinearly dependent predictions, often seen in machine learning. Applying the proposed test on models trained on large-scale functional brain connectivity data (N= 1,865) (i) reveals previously unreported confounders and (ii) shows that state-of-the-art confound mitigation approaches may fail preventing confounder bias in several cases.
conclusionsThe proposed test (implemented in the package mlconfound; https://mlconfound.readthedocs.io) can aid the assessment and improvement of the generalizability and validity of predictive models and, thereby, fosters the development of clinically useful machine learning biomarkers.
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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.