As a task requiring strong professional experience as supports, predictive biomedical
intelligence cannot be separated from the support of a large amount of external domain …

This study describes the model design of the NCUEE system for the MEDIQA challenge at
the ACL-BioNLP 2019 workshop. We use the BERT (Bidirectional Encoder Representations …

The emergence of large language models (LLMs) has marked a significant milestone in the
evolution of natural language processing. With the expanded use of LLMs in multiple fields …

With advances in deep learning and natural language processing (NLP), the analysis of
medical texts is becoming increasingly important. Nonetheless, despite the importance of …

Making inference on clinical texts is a task which has not been fully studied. With the newly
released, expert annotated MedNLI dataset, this task is being boosted. Compared with open …

Recently, the increasing demand for superior medical services has highlighted the
discrepancies in the medical infrastructure. With big data, especially texts, forming the …

Integrating large language models (LLMs) into healthcare presents potential but faces
challenges. Directly pre-training LLMs for domains like medicine is resource-heavy and …

Objective Most existing fine-tuned biomedical large language models (LLMs) focus on
enhancing performance in monolingual biomedical question answering and conversation …

Pretrained language models augmented with in-domain corpora show impressive results in
biomedicine and clinical Natural Language Processing (NLP) tasks in English. However …

Medical named entity recognition (NER), a fundamental task of medical information
extraction, is crucial for medical knowledge graph construction, medical question answering …