Abstract:Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures influence downstream risk. We constructed a fixed-window EHR cohort (90-day observation, 730-day prediction; N=81401; dialysis/ESRD prevalence: 1.1%) and modeled sequences of diagnoses, procedures, and medications with kidney laboratory trends (creatinine, BUN, eGFR). A transformer-based causal multi-head model was trained to estimate drug- and ingredient-level average treatment effects (ATEs) using counterfactual exposure removal and insertion under a full medication history setup. On test set, predictive performance reached an AUC of 0.694 and PR-AUC of 0.094. At the selected decision threshold (0.883), the model achieved an F1 score of 0.201 with a Brier score of 0.018. Post-hoc causal analyses of lab changes (eGFR, creatinine, BUN) using IPTW, AIPW, naive, and covariate-adjusted OLS methods assessed clinical directionality. Results showed partial protective-direction support for ACE/ARB exposures and worsening-direction signals for loop diuretics.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.24547 [cs.LG] |
| (or arXiv:2604.24547v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.24547 arXiv-issued DOI via DataCite (pending registration) |
From: Kalyani Pande [view email]
[v1]
Mon, 27 Apr 2026 14:41:37 UTC (479 KB)
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