# Medical NLP

This model is based on BiomedBERT and is used for sentence segmentation of MIMIC-III clinical records, predicting BIO annotations.

A multimodal biomedical model fine-tuned based on Llama-3-8B-Instruct, supporting text and image processing, suitable for biomedical research and clinical applications.

A clinical named entity recognition (NER) model fine-tuned on the i2b2-2010 dataset, specifically designed to identify three types of clinical entities: diseases, treatments, and examinations.

ClinicalT5 is a generative language model based on the T5 architecture. It has been pre - trained specifically for clinical text processing and is suitable for natural language processing tasks in the medical field.

A clinical text processing model fine-tuned based on Bio_ClinicalBERT, performing well in F1 score

A fine-tuned model based on BioBERT for the biomedical domain, achieving an F1 score of 0.8621 on specific tasks

The CORe model is based on the BioBERT architecture, specifically pretrained on clinical records, disease descriptions, and medical literature for predicting in-hospital mortality risk.

Bioformer-8L is a biomedical domain named entity recognition model fine-tuned on the NCBI disease dataset, specializing in disease entity recognition.