Apollo builds unified multimodal temporal patient embeddings from 25 billion records across 28 modalities and demonstrates forecasting on 322 prognosis and retrieval tasks including 5-year disease onset prediction.
From ehrs to patient pathways: Scalable modeling of longitudinal health trajectories with llms
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EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.
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A multimodal and temporal foundation model for virtual patient representations at healthcare system scale
Apollo builds unified multimodal temporal patient embeddings from 25 billion records across 28 modalities and demonstrates forecasting on 322 prognosis and retrieval tasks including 5-year disease onset prediction.
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EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records
EHR-RAGp is a retrieval-augmented EHR foundation model that employs prototype-guided retrieval to dynamically integrate relevant historical patient context, outperforming prior models on clinical prediction tasks.