Scholarlink

This paper introduces MediNet, a novel framework designed to facilitate healthcare navigation by constructing and utilizing medical knowledge graphs derived from discharge summaries. MediNet incorporates state-of-the-art Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER) facilitated by the Stanza library. By leveraging NER, MediNet extracts structured information from unstructured discharge summaries, covering essential aspects like diagnoses, treatments, test and outcomes. It integrates the Unified Medical Language System (UMLS) for entity normalization and employs a meticulous approach to relation extraction from section-wise entities. The implementation of MediNet signifies a notable progression in healthcare navigation and decision-making processes, providing substantial benefits to both patients and healthcare providers.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MediNet: Navigating Health Care with the Medical Knowledge Graphs from Discharge Summaries

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Ankita Thakur,
  • Shalini Tripathi,
  • Aditi Sharan

摘要

This paper introduces MediNet, a novel framework designed to facilitate healthcare navigation by constructing and utilizing medical knowledge graphs derived from discharge summaries. MediNet incorporates state-of-the-art Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER) facilitated by the Stanza library. By leveraging NER, MediNet extracts structured information from unstructured discharge summaries, covering essential aspects like diagnoses, treatments, test and outcomes. It integrates the Unified Medical Language System (UMLS) for entity normalization and employs a meticulous approach to relation extraction from section-wise entities. The implementation of MediNet signifies a notable progression in healthcare navigation and decision-making processes, providing substantial benefits to both patients and healthcare providers.