AMR Curator-An Interactive Platform for Analysing AMR Literature Using NLP

Background: The rising issue of Antimicrobial Resistance (AMR) poses a major threat to global health. Objectives: This study introduces a Streamlit application that helps analyze and visualize trends in AMR literature using natural language processing and interactive data visualization. Materials and Methods: We curated a PubMed dataset comprising abstracts published from 2015 to 2025 and used advanced techniques such as topic modeling, named entity recognition, and keyword analysis. We processed 154608 AMR-related abstracts, identified five main research themes by topic modelling, categorized biomedically relevant entities into key pathogens, antimicrobial agents, and geographical entities using NER. Results: Our user-friendly interface represents literature-based AMR analysis tools that combines multiple analytical methods into a single platform. The platform is freely accessible at [https://d-k-02-amr-dashboard-app-qdprm0. streamlit.app/]. Conclusion: This tool offers researchers, epidemiologists, and policymakers an interactive resource for tracking research trends and making informed decisions in antimicrobial stewardship.