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A novel Multi-Level Refined (MLR) knowledge graph design and chatbot system for healthcare applications.
Hsueh, Huei-Chia; Chien, Shuo-Chen; Huang, Chih-Wei; Yang, Hsuan-Chia; Iqbal, Usman; Lin, Li-Fong; Jian, Wen-Shan.
Afiliación
  • Hsueh HC; Department of Pharmacy, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Chien SC; Department of Artificial Intelligence in Medicine, Professional Master Program, Taipei Medical University, Taipei, Taiwan.
  • Huang CW; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
  • Yang HC; International Research Center for Health Information Technology, School of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Iqbal U; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
  • Lin LF; International Research Center for Health Information Technology, School of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Jian WS; Department of Health, Health ICT, Tasmania, Australia.
PLoS One ; 19(1): e0296939, 2024.
Article en En | MEDLINE | ID: mdl-38295121
ABSTRACT
Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reconocimiento de Normas Patrones Automatizadas / Destilación Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Reconocimiento de Normas Patrones Automatizadas / Destilación Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Estados Unidos