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Learning tool of chemical molecules for healthcare students based on 3D visualizations, Large Language Models and Linked Open Data

Published: 29 May 2024 Publication History

Abstract

This paper presents a software tool designed to assist healthcare students and teachers in applying case-based learning methodologies for studying chemical compounds and their structures in a three-dimensional (3D) space. The tool utilizes natural language processing algorithms based on Large Language Models (LLM) to extract chemical entities from biomedical text documents. The tool provides students with a starting point for their research in a case-based learning exercise, and by using Large Language Models and Linked Open Data, it shows them a path to follow. The system shows a description of the molecules, a 3D view, a 2D structure information, and a panel with scientific articles about the selected chemical compound obtained from the Scielo portal, a repository of scientific publications. The application has a modular architecture that allows for easy addition of new functionalities in the future. The web interface let users to input the text of the clinical case for processing and studying. The functionality of querying scientific articles can be expanded with new modules to obtain information from other repositories. Overall, this software tool provides a comprehensive and innovative approach to learning chemical molecules for healthcare students, which can help them develop the necessary skills to tackle real-life problems as healthcare professionals.

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  1. Learning tool of chemical molecules for healthcare students based on 3D visualizations, Large Language Models and Linked Open Data

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    ICEEL '23: Proceedings of the 2023 7th International Conference on Education and E-Learning
    November 2023
    220 pages
    ISBN:9798400708732
    DOI:10.1145/3637989
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 29 May 2024

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    Author Tags

    1. artificial intelligence
    2. chatbot
    3. e-learning
    4. large language models
    5. learning bots
    6. learning management system

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