The integration of large language models (LLMs) into clinical medicine represents a major advancement in natural language processing (NLP). We introduce BioMistral-Clinical 7B, a clinical LLM built on BioMistral-7B (Labrak et al., 2024), designed to support continual learning from unstructured clinical notes for real-world tasks such as clinical decision support. Using the augmented-clinical-notes dataset provided by Hugging Face (2024), we apply prompt engineering to transform unstructured text into structured JSON, capturing key clinical information (symptoms, diagnoses, treatments, outcomes). This enables efficient incremental training via self-supervised continual learning (SPeCiaL) (Caccia and Pineau, 2021). Evaluation on MedQA (Jin et al., 2021) and MedMCQA (Pal et al., 2022) shows that BioMistral-Clinical 7B improves accuracy on MedMCQA by nearly 10 points (37.4% vs. 28.0%) over the base model, while maintaining comparable performance on MedQA (34.8% vs. 36.5%). Building on this, we propose the BioMistral-Clinical System, which integrates Retrieval-Augmented Generation (RAG) (Lewis et al., 2020) to enrich responses with relevant clinical cases retrieved from a structured vector database. The full system enhances clinical reasoning by combining domain-specific adaptation with contextual retrieval.

We present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.