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.
citing papers explorer
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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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OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
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.
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Traj-Evolve: A Self-Evolving Multi-Agent System for Patient Trajectory Modeling in Lung Cancer Early Detection
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.
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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.