Why the study?
To develop and validate subtype-specific, fairness-aware ML models integrating clinical and SDoH information to predict 6-month adverse outcomes in HFpEF or HFrEF and assess demographic subgroup error disparities.
Does integrating social determinants of health into machine learning models improve the prediction of 6-month readmission or mortality in patients with HFpEF or HFrEF?
Population
Adult HF hospitalizations with HFpEF or HFrEF from electronic health record data (2016-2022)
Comparison
Models with clinical and SDoH features vs clinical characteristics alone
Design
Retrospective EHR-based cohort study for ML model development and validation
Follow-up
6 months
Key result
Integrating social determinants of health into machine learning models modestly improved the C statistic for 6-month readmission or mortality in HFpEF (0.603 vs 0.586) and HFrEF (0.641 vs 0.637).
Authors
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May enhance equitable HF risk stratification with SDOH integration; leaves open prospective validation before clinical adoption.
Observational
No
Does integrating social determinants of health into machine learning models improve the prediction of 6-month readmission or mortality in patients with HFpEF or HFrEF?
Integrating social determinants of health into machine learning models provides modest discrimination gains and enables subgroup fairness assessment for predicting 6-month outcomes in heart failure patients.
Yeh et al. (2026) conducted an observational in Heart failure (HFpEF and HFrEF). Integration of social determinants of health (SDoH) into machine learning models vs. Models without SDoH was evaluated on Composite outcome of 6-month readmission or mortality. Integrating social determinants of health into machine learning models modestly improved the C statistic for 6-month readmission or mortality in HFpEF (0.603 vs 0.586) and HFrEF (0.641 vs 0.637).
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