ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare.Journal of racial and ethnic health disparities · 2026Pooled it
- Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.JAMIA open · 2026Review
- Using machine learning to predict outcomes following transcarotid artery revascularization.Scientific reports · 2025Article
- Sex-Based Performance Disparities in Machine Learning Algorithms for Cardiac Disease Prediction: Exploratory Study.Journal of medical Internet research · 2024Article
- Information heterogeneity between progress notes by physicians and nurses for inpatients with digestive system diseases.Scientific reports · 2024Article
- Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review.Health affairs (Project Hope) · 2023Article
- To err is machine: Considerations on the clinical impact of machine learning models in patients with unhealthy alcohol use.Alcoholism, clinical and experimental research · 2022Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Unhealthy alcohol use represents a major economic burden and cause of morbidity and mortality in the United States. Implementation of interventions for unhealthy alcohol use depends on the availability and accuracy of screening tools. Our group previously applied methods in natural language processing and machine learning to build a classifier for unhealthy alcohol use. In this study, we sought to evaluate and address bias through the use-case of our classifier. We demonstrated the presence of biased unhealthy alcohol use risk underestimation among Hispanic compared to Non-Hispanic White trauma inpatients, 18- to 44-year-old compared to 45 years and older medical/surgical inpatients, and Non-Hispanic Black compared to Non-Hispanic White medical/surgical inpatients. We further showed that intercept, slope, and concurrent intercept and slope recalibration resulted in minimal or no improvements in bias-indicating metrics within these subgroups. Our results exemplify the importance of integrating bias assessment early into the classifier development pipeline.
Indexed as
Identifiers
35308909PMC8861719W4287540253What 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.