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Simpler Models Outperform Deep Learning for Hospitalization Risk Prediction

Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton· September 1, 2026 View original

Key takeaways

  • For high-stakes clinical predictions, model calibration is as crucial as discrimination.
  • Simpler models like LASSO can outperform complex deep learning in real-world clinical deployment.
  • Robust data infrastructure is essential for effective patient trajectory modeling.
  • Interpretability and calibration should guide model selection in healthcare AI.

Who benefits

HealthcarePharmaceuticalsInsurancePublic Health

Summary

A study comparing deep learning with simpler models for predicting hospitalization risk in elderly patients found that Logistic Regression with LASSO regularization achieved the best discrimination on a held-out test set and was the only model with acceptable calibration for clinical deployment. This highlights the importance of calibration and interpretability over raw discrimination in high-stakes healthcare decisions.

Predicting hospitalization risk in elderly patients with multiple long-term conditions is a critical application for machine learning in healthcare. This research presents a comprehensive patient timeline pipeline using CPRD Aurum data, classifying 260 clinical conditions. The study rigorously benchmarked Temporal Graph Convolutional Neural Networks (TG-CNN) against more interpretable models like Logistic Regression with LASSO regularization and Random Forests for predicting 12-month emergency hospitalization risk. While TG-CNN showed a marginally higher mean AUC-ROC in cross-validation, LASSO achieved the highest discrimination on the held-out test set. Crucially, the study emphasizes that discrimination alone is insufficient for clinical deployment. After Platt calibration, LASSO was the only model demonstrating an acceptable calibration slope, whereas Random Forest and TG-CNN remained substantially miscalibrated. The findings strongly suggest that LASSO, despite not always having the highest raw discrimination, is the most suitable model for direct clinical deployment due to its superior calibration and interpretability, offering valuable lessons for the healthcare and machine learning communities.

Why it matters

Professionals in healthcare AI must prioritize model calibration and interpretability alongside predictive accuracy, especially for high-stakes clinical decision support, as simpler models can often be more reliable and deployable than complex deep learning architectures.

How to implement this in your domain

  1. 1Prioritize model calibration and interpretability alongside discrimination metrics in healthcare AI projects.
  2. 2Evaluate simpler, more interpretable models like LASSO regression before defaulting to complex deep learning.
  3. 3Implement robust data infrastructure for patient trajectory modeling, including comprehensive condition classification.
  4. 4Conduct rigorous empirical scrutiny of model performance on held-out test sets, not just cross-validation.
  5. 5Integrate Platt calibration or similar techniques to ensure model predictions are well-calibrated for clinical use.

Original post by Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton

"arXiv:2608.29419v1 Announce Type: new Abstract: Deep learning architectures are increasingly proposed for patient trajectory modeling in electronic health records (EHRs), yet their advantage over simpler, more interpretable models is rarely subjected to rigorous empirical scrutin…"

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Originally posted by Asra Aslam, Volodymyr Chapman, Maurice M. O'Connell, Aseel S. Abuzour, Michael Abaho, Danushka Bollegala, Gary Leeming, Eduard Shantsila, Andrew Clegg, Lauren E. Walker, Iain Edward Buchan, Samuel D. Relton on X · view source

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