DocNER: Document Chat Assistance with NER
Year: 2024
Type: article
Abstract: The increasing amounts of high volume unstructured medical data, such as research and clinical articles, impede efficient information extraction. Since the medical texts have a complex structure, many times these approaches do not work, leading to significant losses. The current approaches are not robust to a wide spectrum of medical content as they are too specific to the given domain.This study proposes a recent innovative approach that involves the combination of Named Entity Recognition (NER) and Retrieval-Augmented Generation (RAG) to enhance entity extraction and give useful bibliographic information from medical data respectively. The objectives of our method are to improve accuracy, accommodate different kinds of documents, and assist in clinical research and patient care.
Authors: Sahil Baviskar -, Lokesh Deshmukh -, Nishica Kothawade -, Rutuja Uphale -, Manisha Mali -
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404281430