aiqbench analysis
PaReGTA introduces a lightweight LLM-based framework for encoding electronic health records that preserves temporal dynamics without the heavy computational cost of full sequence models. By converting longitudinal events into templated text with explicit time cues, it enables effective patient representation using standard downstream classifiers like LightGBM. This approach addresses the data-hungry nature of traditional sequence models while maintaining high predictive performance on large-scale cohorts.
PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics
arXiv:2602.19661v3 Announce Type: replace 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. The resulting fixed-dimensional patient representations can be used with conventional downstream machine-learning models. To examine factor-level sensitivity, we use PaReGTA-RSS (Representation Shift Score), a prespecified factor-removal analysis that recomputes patient representations after removing clinically defined factor groups and quantifies the resulting change in the fitted logit of a fixed logistic-regression model. We evaluated PaReGTA in a cohort of 39,088 patients with migraine from the All of Us Research Program (AoU) on a retrospective patient-level classification task with an EHR-derived target. In an exploratory comparison on the fixed held-out test cohort of 7,818 patients, PaReGTA-Gap + LightGBM, used as a post hoc analytical reference, had the highest reported AUC, accuracy, and F1 values among the evaluated sparse, BERT-based EHR, and recurrent approaches in this cohort.
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. The resulting fixed-dimensional patient representations can be used with conventional downstream machine-learning models. To examine factor-level sensitivity, we use PaReGTA-RSS (Representation Shift Score), a prespecified factor-removal analysis that recomputes patient representations after removing clinically defined factor groups and quantifies the resulting change in the fitted logit of a fixed logistic-regression model. We evaluated PaReGTA in a cohort of 39,088 patients with migraine from the All of Us Research Program (AoU) on a retrospective patient-level classification task with an EHR-derived target. In an exploratory comparison on the fixed held-out test cohort of 7,818 patients, PaReGTA-Gap + LightGBM, used as a post hoc analytical reference, had the highest reported AUC, accuracy, and F1 values among the evaluated sparse, BERT-based EHR, and recurrent approaches in this cohort.
Machine Learning (cs.LG)
arXiv:2602.19661 [cs.LG]
(or arXiv:2602.19661v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.19661 Focus to learn more arXiv-issued DOI via DataCite
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