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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OphthaDT serializes patient data into narratives and uses LLMs to forecast BCVA trajectories, achieving 6.0% lower MAE than baselines in nAMD and competitive results in DME.
Traj-Evolve combines non-parametric experience retrieval and multi-agent RL with a leave-one-out unification strategy to outperform baselines on lung cancer prediction from up to five years of multimodal EHRs, including in never-smokers.
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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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.