[Paper Review] Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

Ahmad Idrissi-Yaghir, Amin Dada+18 more|arXiv (Cornell University)|Apr 8, 2024

Biomedical Text Mining and OntologiesBiochemistry, Genetics and Molecular Biology3 citations

TL;DR

This paper introduces and evaluates German biomedical and clinical language models fine-tuned via continuous pre-training on 2.4B tokens of translated English medical texts and 3B tokens of German clinical data. Results show that translation-based models match or exceed performance of models trained from scratch on clinical data, demonstrating that domain adaptation through translation and clinical data pre-training significantly boosts performance on German medical NLP tasks.

ABSTRACT

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.

Motivation & Objective

• Address the scarcity of high-quality German medical NLP resources by developing domain-specific language models.
• Investigate whether continuous pre-training on translated or clinical German data improves performance on downstream medical NLP tasks.
• Compare the effectiveness of translation-based pre-training versus private clinical data for training German medical language models.
• Evaluate the performance of newly developed German language models across multiple clinical and biomedical NLP tasks.
• Assess the trade-offs between data privacy, model performance, and accessibility in training German medical language models.

Proposed method

• Continuously pre-trained several German BERT-based models on 2.4 billion tokens of translated PubMed abstracts from English to German.
• Continuously pre-trained additional models on 3 billion tokens of anonymized, de-identified German clinical notes from a large German hospital.
• Fine-tuned the resulting models on five downstream tasks: named entity recognition (NER), multi-label classification, extractive question answering, and two clinical benchmark tasks (RadQA and MedNLI).
• Compared the performance of translation-based and clinical data-based models against general-domain (e.g., GBERT), biomedical, and clinical German models.
• Used standard NLP evaluation metrics such as F1, accuracy, and exact match across all tasks.
• Ensured data privacy by not sharing models trained on proprietary clinical data; only translation-based models were released publicly.

Experimental results

Research questions

• RQ1 Can continuous pre-training on translated biomedical texts effectively adapt general German language models to the clinical domain?
• RQ2 How does performance of models pre-trained on translated data compare to those pre-trained on private clinical data in German medical NLP tasks?
• RQ3 To what extent can translation-based pre-training reduce reliance on sensitive clinical data while maintaining high performance?
• RQ4 In which downstream tasks does clinical-specific pre-training provide a measurable advantage over translation-based pre-training?
• RQ5 Can models trained on public, translated data achieve performance comparable to those trained on large-scale private clinical datasets?

Key findings

• Translation-based models pre-trained on 6 million translated PubMed abstracts achieved performance comparable to large-scale clinical models on most downstream tasks.
• On clinical-specific tasks such as RadQA, models trained on private clinical data outperformed translation-based models, indicating the value of domain-specific data.
• In five evaluated downstream tasks, models pre-trained on clinical data consistently outperformed general-domain German models like GBERT, especially in complex clinical reasoning tasks.
• The performance gap between translation-based and clinical data-based models was smallest in extractive question answering and NER, suggesting that translation data captures essential medical terminology effectively.
• Despite using only 6 million translated abstracts, the translation-based models matched or exceeded the performance of models trained from scratch in several tasks, demonstrating the efficiency of transfer learning.
• The study confirms that continuous pre-training on either clinical data or high-quality translated biomedical texts is a reliable and effective strategy for domain adaptation in German medical NLP.