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About this paper
Sheng Zhang, Qin Liu, Naoto Usuyama, Cliff Wong, Tristan Naumann, Hoifung Poon
Researchers from Microsoft Research empirically demonstrated that scaling laws, similar to those governing large language models, apply to EHR foundation models, identifying predictable performance gains with increased compute and highlighting data volume as a critical scaling bottleneck. Their method achieved improvements in clinical task performance, with ICU mortality ROC AUC increasing from 0.68 to 0.75.
Key insights
- EHR foundation models exhibit predictable scaling behaviors, with validation loss showing parabolic IsoFLOPs curves and power-law relationships, analogous to LLMs.
- A strong log-linear correlation exists between lower validation loss (a general pre-training objective) and improved zero-shot performance on downstream clinical tasks.
- The observed power-law exponents for optimal model size (0.58) and training tokens (0.44) differ from those for natural language LLMs, possibly due to modality differences or data scale.
Summary by alphaXiv.
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