New EHR2Path framework models complete patient hospital pathways from multimodal data

Researchers have developed EHR2Path, a new multimodal framework designed to model and predict complete patient pathways within a hospital setting using electronic health records (EHRs). This system integrates diverse clinical data, including reports, notes, vital signs, and medication data, into a unified temporal representation. EHR2Path employs a Masked Summarization Bottleneck to efficiently process long clinical histories while preserving recent context, improving both performance and token efficiency. Experiments on the MIMIC-IV dataset demonstrated EHR2Path's capability in forecasting next steps and simulating entire in-hospital trajectories, outperforming existing baseline methods. AI

IMPACT Enables more proactive and personalized patient care by forecasting complete in-hospital trajectories from EHR data.

RANK_REASON The cluster contains an academic paper detailing a new modeling framework for healthcare data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Chantal Pellegrini
• EHR2Path
• MIMIC-IV

• paper
• model release

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