International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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DocNER: Document Chat Assistance with NER

Author(s) Sahil Baviskar, Lokesh Deshmukh, Nishica Kothawade, Rutuja Uphale, Manisha Mali
Country India
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.
Keywords Named Entity Recognition (NER), Retrieval-Augmented Generation (RAG), Medical entity extraction, Natural Language Processing (NLP), Unstructured medical data.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-10
Cite This DocNER: Document Chat Assistance with NER - Sahil Baviskar, Lokesh Deshmukh, Nishica Kothawade, Rutuja Uphale, Manisha Mali - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30126
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30126
Short DOI https://doi.org/g8qtk9
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