ArticleThe American journal of bioethics : AJOB2026
Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis.
Article in The American journal of bioethics : AJOB, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- The ethics of data mining in healthcare: challenges, frameworks, and future directions.BioData mining · 2025Review
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5 authors.
Funding
Abstract
This paper discusses ethics-based strategies for mitigating bias in machine learning models used to predict sepsis onset. The first part discusses how various kinds of bias and their potential synergies can reduce predictive accuracy, especially as those biases derive from social determinants of health (SDOHs) and from the design and construction of the predictive model. The second part of the essay discusses how certain ethically-based strategies might mitigate the potential for disparate or unfair treatment produced by these models, not only as they might apply to sepsis but to any syndrome that witnesses the impact of adverse SDOHs on socioeconomically disadvantaged or marginalized populations.
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Registered trials
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