כתבה
arXiv cs.LG ·
PaReGTA: פרקטיקה זמנית לייצוג נוסחאות LLM לניתוח EHR
PaReGTA: A Temporally Aware LLM-Based Patient Representation Framework for EHR Analytics
פרקטיקה LLM חדשה לניתוח EHR, המשתמשת במודלי זמן ומסגרת LangGraph
תקציר מקורי באנגליתarXiv: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 mod
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