Biomedical Named Entity Recognition with Transformers

This is a tutorial on how to annotate and recognize biomedical entities with the Bio-Epidemiology-NER package and the biomedical-ner-all model. Bio-Epidemiology-NER is a Python library built on top of the biomedical-ner-all model to recognize bio-medical entities from a corpus or a medical report. Research Paper: Authors: Shaina Raza, Deepak John Reji, Femi Shajan, Syed Raza Bashir Package: GitHub: Huggingface Hub: This package can recognize over 50 different entity types, including clinical entities (disease, symptoms, risks, effects, drugs, diabetes, respiration, vital signs, and others), as well as non-clinical entities, such as event-based data, social factors that are not clinical factors but are related to health outcomes. Second, with no code changes, this pipeline is simple to use and adaptable to individual methods for a given data type, task, or domain of application. Third, this pipeline can take any free texts, for example, in the form of text or PDF files and parse them for scientific texts. We hope that this package will provide a more transparent and customizable solution for the healthcare industry, helping to educate and encourage more rigorous applications of ML to biomedical analyses. Chapters 00:00 Introduction 00:24 About the model (biomedical-ner-all) 02:19 About the package (Bio-Epidemiology-NER) 03:30 how to use the model 06:09 how to use the package 09:04 Report annotation feature 15:36 Conclusion