Resources

The augsig package is a modular and extensible Python library for physiological signal augmentation. It provides a set of physiologically consistent augmentation methods for biomedical signals, designed to enhance data diversity, improve generalizat…

This dataset comprises 9,606 Synthetic Discharge Summaries generated by GPT-3.5 based on combinations of ICD-10-code descriptions associated with real discharge summaries in MIMIC-IV. As part of the generation process, GPT-3.5 was also tasked to cod…

Precise pain assessment is essential for medical professionals to provide appropriate treatment. However, not every patient can verbalize their pain due to various reasons, such as speech disorders or language barriers. In these cases, medical pract…

This dataset was designed and created to enable advancements in healthcare-focused large language models, particularly in the context of retrieval-augmented clinical question-answering capabilities. Developed using a self-constructed pipeline based …

MIMIC-III and MIMIC-IV are foundational resources for clinical NLP, but their scale is modest relative to the data volumes needed to train language models. Building on recent advances in synthetic data generation through LLM-based rephrasing, we con…

Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental tasks in information extraction, particularly in the biomedical and clinical domains. NER identifies textual mentions of entities, while EN maps these mentions to unique id…