Readable research linked to original sources
Articles
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
7 articles
Newest firstObjectivesTo develop and demonstrate a practical post-hoc, model-agnostic fairness auditing tool that integrates group-level and individual-level fairness assessment for clinical prediction models. Materials and MethodsWe developed EquiLense, a fairness auditing tool that operationalizes three components: group fairness evaluation using established and novel group-level disparity metrics, individual fairness asses…
Open article record in new tab ↗BackgroundIn clinical contexts where disease burden differs across demographic groups, enforcing demographic parity -- equal prediction rates regardless of group -- may reduce screening for the populations that need it most. We demonstrate this using HIV testing prediction as a case study. MethodsUsing the Behavioral Risk Factor Surveillance System (BRFSS) 2024 dataset (N=386,775), we trained four classifiers to p…
Open article record in new tab ↗Algorithmic decision systems mediate access to healthcare, credit, employment and housing, yet individuals who experience adverse decisions face multi-stage barriers when seeking recourse. We formalize these barriers as a series-structured system with 11 empirically parameterized stages across three layers (data integration, data accuracy and institutional access) and prove that single-barrier interventions are bo…
Open article record in new tab ↗While algorithmic fairness research in healthcare has predominantly focused on disparities in model performance, less attention has been given to the underlying data structures that may drive such disparities. High-level fairness metrics often obscure the deeper feature-level dynamics necessary for a critical and context-aware assessment of fairness. To address this gap, we propose and apply a diagnostic framework…
Open article record in new tab ↗Heart failure (HF) prediction models using machine learning (ML) must achieve a balance between performance, fairness, and real-world clinical utility. This paper assesses the potential of ML and DL models in the context of heterogeneous databases (UCI, MIMIC) and aims to derive applicable schemes for equitable deployment in healthcare. Although the Transformer models depicted notable AUC-ROC in the UCI data (0.98…
Representation bias in health data can lead to unfair decisions and compromise the generalisability of research findings. As a consequence, underrepresented subpopulations, such as those from specific ethnic backgrounds or genders, do not benefit equally from clinical discoveries. Several approaches have been developed to mitigate representation bias, ranging from simple resampling methods, such as SMOTE, to recen…
BackgroundAccessing specialist secondary mental health care in the NHS in England requires a referral, usually from primary or acute care. Community mental health teams triage these referrals deciding on the most appropriate team to meet patients needs. Referrals require resource-intensive review by clinicians and often, collation and review of the patients history with services captured in their electronic health…