Name_Entity_Recognition and NER-medical-text

About Name_Entity_Recognition

R-aryan/Name_Entity_Recognition

This repository contains model for NER trained on clinical data to extract names of diseases from unstructured text. Named-entity recognition (NER) (also known as entity extraction) is a sub-task of information extraction that seeks to locate and classify named entity mentions in unstructured text into pre-defined categories such as the person names, organizations etc.

About NER-medical-text

iajaykarthick/NER-medical-text

This project is to develop a named entity recognition (NER) model to identity medical entities such as diseases, symptoms, treatments in the unstructured medical text written in natural language.

Automatically scans and extracts key medical information like diseases, symptoms, and treatments from free-form medical notes, research papers, or patient records. It takes raw, unstructured medical text as input and identifies and categorizes these important entities. This helps medical researchers, clinicians, and data analysts quickly find specific information in large volumes of text.