warfarisk-autogluon-1780

Beyond MAE: Phase 3 AutoGluon (IWPC-1780)

<p align="center"> <img src="https://placehold.co/800x200/dbeafe/1e40af?text=Beyond+MAE+%E2%80%94+AutoGluon+IWPC-1780" alt="Beyond MAE: Phase 3 AutoGluon IWPC-1780"> </p>

This repository contains the Phase 3 winning model from the pipeline described in "Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models", a nine-phase reproducible ML pipeline for warfarin dose prediction, trained on the IWPC-1780 cohort. An AutoGluon extreme_quality predictor on the combined clinical+genetic feature set reaches MAE 7.959, R² 0.480, PW20 0.497, the best of 9 architectures × 3 feature sets evaluated on this cohort, with the stacking ensemble (R² 0.479) essentially tied for second.

By: Hassan Barmandah, Omar Abdullah Bawazir, Siraj Aldeen Marghalani, Moath Shaat, Abdullah N. Alkattan, and Mariam M. AlEissa (corresponding author), Alfaisal University, Riyadh (with Umm Al-Qura University, Saudi Electronic University, Ministry of Health, Public Health Authority, King Khaled Eye Specialist Hospital Research Center, King Abdullah Petroleum Studies and Research Center)

This is a research artifact, not a validated clinical tool. It has not been evaluated prospectively and has no regulatory status.

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Model Description

This model is an AutoGluon TabularPredictor fit with the extreme_quality preset on IWPC-1780 (n=1,780, n_test=356, fixed 80/20 patient-level split, seed 20260725), using the combined feature set (clinical + CYP2C9/VKORC1 genotype consensus). IWPC-1780 uses binary ancestry flags (Black, Asian) and pre-z-scored continuous features, and is evaluated independently rather than pooled with IWPC-6256. This is the best-performing model of 9 architectures evaluated across 3 feature sets on this cohort in the Phase 3 nested ablation.

Key Contributions

• Best Single Model on IWPC-1780: MAE 7.959 / R² 0.480 / PW20 0.497, narrowly ahead of the stacking ensemble (R² 0.479) and TabPFN (R² 0.477), notably beating every individually-tuned model including CatBoost/TabPFN on this smaller cohort
• Combined-Feature Advantage: beats the same architecture family's clinical-only (R² 0.296) and genetic-only (R² 0.313) results on this cohort
• Independent Second-Cohort Replication: run with the identical AutoGluon configuration used on IWPC-6256, confirming the combined-feature advantage is not an artifact of one dataset

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🚀 How to Use

from autogluon.tabular import TabularPredictor
from huggingface_hub import snapshot_download

local_dir = snapshot_download("HassanB4/warfarisk-autogluon-1780")
predictor = TabularPredictor.load(local_dir)
prediction = predictor.predict(your_dataframe)  # combined clinical+genetic features, see Training Data below

Input features: age, weight, height (pre-z-scored, not raw units; do not feed raw clinical values without re-harmonizing scale), binary ancestry flags (Black, Asian), CYP2C9 consensus, VKORC1.AA/VKORC1.AG genotype flags, amiodarone/enzyme-inducer comedication flags. See HasanBGit/WarfaRisk's src/warfarisk/phase3_genetics_ablation.py for the exact feature-engineering pipeline.

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⚙️ Training Procedure

Training Data

Hyperparameters

Same excluded-model-type configuration as the IWPC-6256 run, applied for consistency across cohorts. Full run notes in docs/RESULTS.md and docs/KNOWN_ISSUES.md.

Models Compared (combined feature set, IWPC-1780)

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📊 Evaluation Results

PW20 = proportion of predictions within 20% of the true dose (IWPC's own clinical-acceptability threshold is ≥0.50: this model comes within one point of clearing it, the closest of any model evaluated in this project).

Comparison Across Cohorts

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⚠️ Limitations

• Research-Only: not validated prospectively, no regulatory status
• Pre-Scaled Features: this cohort's continuous features (age/weight/height) are pre-z-scored at source; do not pool raw inputs with IWPC-6256 without re-harmonizing scale
• Coarser Ancestry Encoding: IWPC-1780 only distinguishes Black/Asian/Other, unlike IWPC-6256's 14 labels; see docs/RESULTS.md for this project's Phase 4/5 ancestry-fairness and calibration findings on this cohort
• No Raw Data Shipped: the code repository does not redistribute IWPC data; see data/DATA.md

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🙏 Acknowledgements

We thank the PharmGKB / International Warfarin Pharmacogenetics Consortium for the IWPC dataset, and the AutoGluon team for the AutoML framework this model was built with.

Related Links

• AutoGluon
• PharmGKB
• Code repository

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📜 Citation

This model is described in the following article, published in the Journal of Personalized Medicine.

Barmandah, H.; Bawazir, O.A.; Marghalani, S.A.; Shaat, M.; Alkattan, A.N.; AlEissa, M.M. Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models. J. Pers. Med. 2026, 16 (10), 531. https://doi.org/10.3390/jpm16100531

@Article{jpm16100531,
    author={Barmandah, Hassan and Bawazir, Omar A. and Marghalani, Siraj Aldeen and Shaat, Muath and Alkattan, Abdullah N. and AlEissa, Mariam M.},
    title={Beyond Mean Absolute Error: Ancestry-Stratified Calibration and Explainability for Warfarin Dosing Models},
    journal={Journal of Personalized Medicine},
    volume={16},
    year={2026},
    number={10},
    article-number={531},
    url={https://www.mdpi.com/2075-4426/16/10/531},
    issn={2075-4426},
    doi={10.3390/jpm16100531}
}

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📄 License

This project is licensed under the Apache 2.0 License. This model was fit on IWPC data; the fitted artifact and code are shared under Apache-2.0, but the underlying IWPC dataset is not redistributed by this repository; see HasanBGit/WarfaRisk's data/DATA.md for how to obtain it.