Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings

TL;DR

This study shows that large language model embeddings from routine clinical records can effectively predict post-traumatic epilepsy early, offering a resource-efficient alternative to neuroimaging.

Contribution

The paper introduces a novel approach using pretrained LLMs as feature extractors for early PTE prediction from clinical records, improving performance over traditional tabular data methods.

Findings

LLM embeddings outperform tabular features in predictive accuracy.

Fusion of tabular data and LLM embeddings yields the highest AUC-ROC of 0.892.

Key predictors include acute seizures, injury severity, and ICU stay.

Abstract

Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of PTE remains challenging due to heterogeneous clinical data, limited positive cases, and reliance on resource-intensive neuroimaging data. We investigate whether routinely collected acute clinical records alone can support early PTE prediction using language model-based approaches. Methods: Using a curated subset of the TRACK-TBI cohort, we developed an automated PTE prediction framework that implements pretrained large language models (LLMs) as fixed feature extractors to encode clinical records. Tabular features, LLM-generated embeddings, and hybrid feature representations were evaluated using gradient-boosted tree classifiers under stratified cross-validation. Results: LLM embeddings achieved performance improvements by capturing contextual clinical information compared to using tabular features alone. The best performance was achieved by a modality-aware feature fusion strategy combining tabular features and LLM embeddings, achieving an AUC-ROC of 0.892 and AUPRC of 0.798. Acute post-traumatic seizures, injury severity, neurosurgical intervention, and ICU stay are key contributors to the predictive performance. Significance: These findings demonstrate that routine acute clinical records contain information suitable for early PTE risk prediction using LLM embeddings in conjunction with gradient-boosted tree classifiers. This approach represents a promising complement to imaging-based prediction.