Paper deep dive
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data
Mitul Goswami, Romit Chatterjee, Arif Ahmed Sekh
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%
Last extracted: 7/13/2026, 3:34:04 AM
Summary
The paper introduces FairMed-XGB, a framework for mitigating gender-based prediction bias in critical care machine learning models. It combines a fairness-aware loss function (Statistical Parity Difference, Theil Index, Wasserstein Distance) with Bayesian optimization and an XGBoost classifier. Evaluated on MIMIC-IV-ED and eICU datasets, the framework significantly reduces bias metrics with minimal accuracy loss and uses SHAP for explainability.
Entities (10)
Relation Signals (11)
FairMed-XGB → evaluatedon → MIMIC-IV-ED
confidence 95% · Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED
FairMed-XGB → evaluatedon → eICU
confidence 95% · Post-mitigation evaluation on seven clinically distinct cohorts derived from ... eICU databases
FairMed-XGB → uses → XGBoost
confidence 95% · The framework integrates a fairness-aware loss function ... into an XGBoost classifier.
FairMed-XGB → uses → SHAP
confidence 93% · SHAP-based explainability reveals that the framework diminishes reliance on gender-proxy features
FairMed-XGB → incorporates → Wasserstein Distance
confidence 92% · The framework integrates a fairness-aware loss function combining ... and Wasserstein Distance
FairMed-XGB → incorporates → Statistical Parity Difference
confidence 92% · The framework integrates a fairness-aware loss function combining Statistical Parity Difference
FairMed-XGB → incorporates → Theil Index
confidence 92% · The framework integrates a fairness-aware loss function combining ... Theil Index
FairMed-XGB → optimizes → Bayesian Search
confidence 90% · jointly optimised via Bayesian Search into an XGBoost classifier.
Cypher Suggestions (0)
No Cypher suggestions yet.
Abstract
Abstract:Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly optimised via Bayesian Search into an XGBoost classifier. Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED and eICU databases demonstrates substantial bias reduction: Statistical Parity Difference decreases by 40 to 51 percent on MIMIC-IV-ED and 10 to 19 percent on eICU; Theil Index collapses by four to five orders of magnitude to near-zero values; Wasserstein Distance is reduced by 20 to 72 percent. These gains are achieved with negligible degradation in predictive accuracy (AUC-ROC drop <0.02). SHAP-based explainability reveals that the framework diminishes reliance on gender-proxy features, providing clinicians with actionable insights into how and where bias is corrected. FairMed-XGB offers a robust, interpretable, and ethically aligned solution for equitable clinical decision-making, paving the way for trustworthy deployment of AI in high-stakes healthcare environments.
Tags
Links
- Source: https://arxiv.org/abs/2603.14947v1
- Canonical: https://arxiv.org/abs/2603.14947v1
Trouble viewing inline? Open PDF directly →
Full Text
4,914 characters extracted from source content.
Expand or collapse full text
Skip to main content arXiv is now an independent nonprofit! Learn more × Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2603.14947v1 (cs) [Submitted on 16 Mar 2026] Title:FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data Authors:Mitul Goswami, Romit Chatterjee, Arif Ahmed Sekh View a PDF of the paper titled FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data, by Mitul Goswami and 2 other authors View PDF Abstract:Machine learning models deployed in critical care settings exhibit demographic biases, particularly gender disparities, that undermine clinical trust and equitable treatment. This paper introduces FairMed-XGB, a novel framework that systematically detects and mitigates gender-based prediction bias while preserving model performance and transparency. The framework integrates a fairness-aware loss function combining Statistical Parity Difference, Theil Index, and Wasserstein Distance, jointly optimised via Bayesian Search into an XGBoost classifier. Post-mitigation evaluation on seven clinically distinct cohorts derived from the MIMIC-IV-ED and eICU databases demonstrates substantial bias reduction: Statistical Parity Difference decreases by 40 to 51 percent on MIMIC-IV-ED and 10 to 19 percent on eICU; Theil Index collapses by four to five orders of magnitude to near-zero values; Wasserstein Distance is reduced by 20 to 72 percent. These gains are achieved with negligible degradation in predictive accuracy (AUC-ROC drop <0.02). SHAP-based explainability reveals that the framework diminishes reliance on gender-proxy features, providing clinicians with actionable insights into how and where bias is corrected. FairMed-XGB offers a robust, interpretable, and ethically aligned solution for equitable clinical decision-making, paving the way for trustworthy deployment of AI in high-stakes healthcare environments. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.14947 [cs.LG] (or arXiv:2603.14947v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2603.14947 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Romit Chatterjee [view email] [v1] Mon, 16 Mar 2026 07:57:40 UTC (622 KB) Full-text links: Access Paper: View a PDF of the paper titled FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data, by Mitul Goswami and 2 other authorsView PDF view license Current browse context: cs.LG < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from