NLP modeling recommendations for restricted data availability in clinical settings

Abstract

Our study provides evidence-based recommendations for clinical NLP practitioners on selecting modeling paradigms based on data availability. We highlight the importance of considering data availability, task complexity, and institutional maturity when designing and training clinical NLP models. Our findings can inform the development of effective clinical NLP solutions in real-world settings.

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