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Open access Aug 2026

Can GPT Be Used as an Alternative Prediction Model to Traditional Machine Learning and Neural Networks on Low-Volume Clinical Data?

The proposed GPT2-based table-to-text framework provides a practical and clinically interpretable approach for disease prediction from limited structured healthcare data and demonstrates strong potential for early risk detection, transparent clinical decision support, and reliable deployment in real-world low-resource...

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Preprint Aug 2026

Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records

BERT-LER is presented, a BERT-style model for coded EHR timelines pretrained and fine-tuned from a de-identified EHR dataset of 75 million patients, that encodes laboratory test results as discrete tokens while retaining graded information through percentile-based binning, paired with Integrated Gradients for token-lev...

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#artificial intelligence Preprint Aug 2026

Scalable Clinical Data Infrastructure and Comparative ML Evaluation for Hospitalisation Risk Prediction in Elderly Patients with Multiple Long-Term Conditions using CPRD

It is argued that LASSO, not the highest-discriminating model, is the model best suited to direct clinical deployment, and lessons for the machine learning and healthcare community regarding data infrastructure, model selection, and value of calibration and interpretability in high-stakes decision support are presented...

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Open access Sep 2026

Rethinking Input Complexity in Transformer-Based Clinical Prediction: Implications for Feature Dimensionality and Sequence Length in Longitudinal EHR Data

Transformer-based prediction models maintained strong performance across reduced feature sets, while dimensionality reduction modestly affected calibration at the highest risk levels, moderate sequence-length reduction substantially reduced computational burden with limited change in overall discrimination.

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Open access Sep 2026

Less Can Be Better: Decomposing Clinical Data Modalities in Large Language Model-based Healthcare Applications

The benefits of multimodal data integration are task-dependent and healthcare LLMs should examine clinical data modalities according to specific tasks for efficient integration, and provide practical guidance for designing efficient clinical decision support systems.

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