A new research paper explores the effectiveness of Joint-Embedding Predictive Architectures (JEPAs) for learning representations from longitudinal electronic health records (EHRs). Despite stable training, the JEPA formulations investigated in the study consistently underperformed standard masked language modeling (MLM) on the MIMIC-IV dataset. The findings suggest that while latent space prediction is achievable, it is not sufficient for competitive EHR representation learning, highlighting challenges in defining clinically relevant latent targets. AI
IMPACT Investigates alternative representation learning methods for healthcare data, potentially impacting future AI applications in clinical settings.
RANK_REASON Research paper published on arXiv detailing an investigation into machine learning objectives for EHR data. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Ehrs
- Joint-Embedding Predictive Architectures
- Masked language modelling
- MIMIC-IV
- transformer encoders
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