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

Rethinking input complexity in transformer-based clinical prediction: implications for feature dimensionality and sequence length in longitudinal electronic health record data

Abstract Objectives Transformer-based models for clinical prediction using longitudinal electronic health record (EHR) data are often developed with large feature sets and long patient histories under the assumption that more data improves performance. However, high-dimensional inputs and long sequences increase comput...

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...

Retrieval-aligned Tabular Foundation Models Enable Robust Clinical Risk Prediction in Electronic Health Records Under Clinically Realistic Constraints

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...

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.

Conceptualising heart disease prediction through a unified framework combining clinical theory and machine learning models

The results show that it is feasible to make better predictions and gain valuable insights by merging these two types of data and that integrating unstructured data allows for a more holistic view of patient health, leading to earlier detection, personalized interventions, and improved decision-making in clinical setti...