# PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information Source: [PaReGTA: An LLM-based EHR Data Encoding Approach to Capture Temporal Information]() Collection index: [llms.txt]() Data updated: 2026-03-04T05:00:12.634Z Authors: Kihyuk Yoon, Lingchao Mao, Catherine Chong, Todd J. Schwedt, Chia-Chun Chiang, Jing Li Published: 2026-02-23T10:09:50.000Z ## Abstract Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence models can be costly and data-hungry. We propose PaReGTA, an LLM-based encoding framework that (i) converts longitudinal EHR events into visit-level templated text with explicit temporal cues, (ii) learns domain-adapted visit embeddings via lightweight contrastive fine-tuning of a sentence-embedding model, and (iii) aggregates visit embeddings into a fixed-dimensional patient representation using hybrid temporal pooling that captures both recency and globally informative visits. Because PaReGTA does not require training from scratch but instead utilizes a pre-trained LLM, it can perform well even in data-limited cohorts. Furthermore, PaReGTA is model-agnostic and can benefit from future EHR-specialized sentence-embedding models. For interpretability, we introduce PaReGTA-RSS (Representation Shift Score), which quantifies clinically defined factor importance by recomputing representations after targeted factor removal and projecting representation shifts through a machine learning model. On 39,088 migraine patients from the All of Us Research Program, PaReGTA outperforms sparse baselines for migraine type classification while deep sequential models were unstable in our cohort. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Migraine type classification on AoU (test)](): Migraine type classification; AoU (test); Accuracy: 92.39. ## Original sources and implementation [Original paper]() [PDF]()