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arXiv Machine Learning By Kihyuk Yoon, Lingchao Mao, Catherine Chong, Todd J. Schwedt, Chia-Chun Chiang, Jing Li

PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics

Read the original on arXiv Machine Learning →

PaReGTA is a large‑language‑model based framework that transforms longitudinal electronic health record events into visit‑level templated text with explicit temporal cues, learns visit embeddings through lightweight contrastive fine‑tuning, and aggregates them into fixed‑dimensional patient representations using hybrid temporal pooling. These representations can be fed into conventional machine‑learning models. In a cohort of 39,088 migraine patients from the All of Us Research Program, PaReGTA combined with LightGBM achieved the highest AUC, accuracy, and F1 scores on a held‑out test set compared to sparse, BERT‑based, and recurrent approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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