ArticleJournal of biomedical informatics2023
Considerations for using predictive models that include race as an input variable: The case study of lung cancer screening.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- AMPREDICT Mobility-4: A Novel Four Category Mobility Outcome Prediction Model for Patients Undergoing Vascular Amputation.European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery · 2025Article
- Development and Validation of PT-PENCIL: The Physical Therapy Frequency Clinical Decision Support Tool to Increase Hospital Discharge to Home.Physical therapy · 2025Article
- Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates.Annual review of public health · 2025Review
- Individual- and Group-Level Disparities Between Racial and Ethnic Groups in Lung Cancer Screening Eligibility Criteria.JAMA network open · 2025Article
- Lightening the Load: Generative AI to Mitigate the Burden of the New Era of Obesity Medical Therapy.JMIR diabetes · 2024Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
Indiscriminate use of predictive models incorporating race can reinforce biases present in source data and lead to an exacerbation of health disparities. In some countries, such as the United States, there is therefore a push to remove race from prediction models; however, there are still many prediction models that use race as an input. Biomedical informaticists who are given the responsibility of using these predictive models in healthcare environments are likely to be faced with questions like how to deal with race covariates in these models. Thus, there is a need for a pragmatic framework to help model users think through how to include race in their chosen model so as to avoid inadvertently exacerbating disparities. In this paper, we use the case study of lung cancer screening to propose a simple framework to guide how model users can approach the use (or non-use) of race inputs in the predictive models they are tasked with leveraging in electronic health records and clinical workflows.
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What 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.