面向辩论场景的中文医学知识图谱数据集

该数据集面向多方会诊场景的辩论和协商决策任务需求，涵盖结构化的医疗数据。知识图谱数据来源于公开的中文医学知识图谱CMeKG(Chinese Medical Knowledge Graph)，由北京大学、郑州大学等科研机构的研究人员基于大规模医学文本数据，结合自然语言处理与文本挖掘技术，以人机结合的方式研发而成。该数据集主要内容包括医疗中心词、药物用法用量、药物分类等信息。该数据集旨在提升会诊中辩论和协商决策的准确性和专业性，并为相关疾病的诊断辅助工具开发奠定数据基础。这不仅有助于推动人工智能技术在医疗领域的应用，也为医学研究提供了宝贵的资源。

This dataset is tailored for debate and collaborative decision-making tasks in multi-party consultation scenarios, covering structured medical data. The knowledge graph data is sourced from the publicly available Chinese Medical Knowledge Graph (CMeKG), which was developed by researchers from Peking University, Zhengzhou University and other research institutions via a human-machine collaborative approach, based on large-scale medical text data combined with natural language processing and text mining technologies. The main contents of this dataset include medical core terms, drug usage and dosage, drug classification and other relevant information. This dataset aims to enhance the accuracy and professionalism of debate and collaborative decision-making during consultations, and lays a data foundation for the development of diagnostic assistance tools for related diseases. This not only helps promote the application of artificial intelligence technology in the medical field, but also provides a valuable resource for medical research.

搜集汇总

背景与挑战

背景概述

该数据集面向医疗会诊中的辩论和协商决策场景，基于公开的中文医学知识图谱CMeKG构建，包含医疗中心词、药物用法用量等结构化信息。它旨在提升会诊辩论的专业性和准确性，为医疗诊断辅助工具开发提供数据基础，推动人工智能在医学领域的应用。