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Summary

Lang1, a new language model, excels at healthcare AI tasks by using in-domain pretraining on electronic health records. It significantly outperforms larger models, demonstrating the need for specialized healthcare AI training.

Keywords:
Electronic Health Recordsclinical prediction tasksdomain-specific modelsfinetuningoperational predictionpretraining

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Healthcare Informatics

Background:

  • Operational decisions in healthcare require specialized predictive models.
  • Current clinical Natural Language Processing (NLP) efforts often prioritize medical knowledge benchmarks over operational tasks.
  • Electronic Health Records (EHR) contain valuable data for healthcare AI.

Purpose of the Study:

  • To introduce Lang1, a family of language models specifically pretrained for healthcare applications.
  • To evaluate Lang1's performance on realistic healthcare operational tasks.
  • To demonstrate the effectiveness of in-domain pretraining and fine-tuning for healthcare AI.

Main Methods:

  • Pretraining Lang1 models (100M-7B parameters) on a large corpus of clinical EHR tokens from NYU Langone Health and internet tokens.
  • Evaluating Lang1 on the REalistic Medical Evaluation (ReMedE) suite, comprising five tasks derived from 668,331 EHR notes.
  • Comparing Lang1's performance against general-purpose and biomedical models in zero-shot and fine-tuned settings.

Main Results:

  • In zero-shot settings, both general-purpose and biomedical models underperformed on four out of five tasks.
  • After fine-tuning, Lang1-1B outperformed larger generalist models by up to 70x and zero-shot models by up to 671x.
  • Joint multi-task fine-tuning facilitated cross-task transfer, with Lang1-1B showing effectiveness on unseen tasks and external health systems.

Conclusions:

  • Effective healthcare AI necessitates in-domain pretraining using clinical data.
  • Supervised fine-tuning is crucial for optimizing language model performance in healthcare.
  • Evaluation beyond proxy benchmarks is essential for assessing true healthcare AI capabilities.