ArticleProceedings of machine learning research2022
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 citations in OpenAlex.
- Representational Veracity in Data Science Health Research: Targets, Proxies, Labels, and Descriptors.Journal of medical Internet research · 2026Article
- Dataset effects outweigh algorithmic effects in determining fairness of healthcare machine learning.NPJ digital medicine · 2026Article
- Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- Time-to-Event Pretraining for 3D Medical Imaging.... International Conference on Learning Representations · 2025Article
- From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.Proceedings of machine learning research · 2024Article
- Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.Journal of biomedical informatics · 2024Article
- Leveraging Generative AI for Clinical Evidence Synthesis Needs to Ensure Trustworthiness.ArXiv · 2024Article
- Racial differences in laboratory testing as a potential mechanism for bias in AI: A matched cohort analysis in emergency department visits.PLOS global public health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight
Identifiers
37152303PMC10162497W4320702157What OpenQuestion holds
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.