NLP modeling recommendations for restricted data availability in clinical settings

Abstract

Background: Clinical decision-making in healthcare often relies on unstructured text data, which can be challenging to analyze using traditional methods. Natural Language Processing (NLP) has emerged as a promising solution, but its application in clinical settings is hindered by restricted data availability and the need for domain-specific knowledge. Methods: We conducted an experimental analysis to evaluate the performance of various NLP modeling paradigms on multiple clinical NLP tasks in Spanish. These tasks included referral prioritization and referral specialty classification. We simulated three clinical settings with varying levels of data availability and evaluated the performance of four foundation models. Results: Clinical-specific pre-trained language models (PLMs) achieved the highest performance across tasks. For referral prioritization, Clinical PLMs attained an 88.85 % macro F1 score when fine-tuned. In referral specialty classification, the same models achieved a 53.79 % macro F1 score, surpassing domain-agnostic models. Continuing pre-training with environment-specific data improved model performance, but the gains were marginal compared to the computational resources required. Few-shot learning with large language models (LLMs) demonstrated lower performance but showed potential in data-scarce scenarios. Conclusions: 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.