基于医疗知识图谱的交互式智能导诊系统
作者:
基金项目:

国家重点研发计划(2017YFB1002303)


Interactive Intelligent Diagnosis Guidance System Based on Medical Knowledge Graph
Author:
  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [19]
  • |
  • 相似文献 [20]
  • | | |
  • 文章评论
    摘要:

    针对在线问诊中患者主诉医疗信息表述多样化, 医疗知识利用不足的问题, 本文设计实现了基于医疗知识图谱的交互式智能导诊系统. 该系统引入医疗知识图谱提供导诊知识, 通过实体识别和实体链接技术规范化主诉文本中的医疗表述, 利用医疗实体生成知识图谱子图并获取子图语义信息, 融合子图和患者主诉的语义信息得到科室置信度. 当推荐科室置信度低时, 通过多轮交互问询的方式补充患者症状信息, 最终给出推荐科室. 该系统能够为建立快速精准智能医疗体系提供技术支持, 有效提升导诊效率, 缓解医疗资源紧张.

    Abstract:

    Diversified expression of medical information and inefficient utilization of medical knowledge are encountered in online consultations. This study designs and implements an interactive intelligent diagnosis guidance system based on medical knowledge graphs. The system introduces medical knowledge graphs to provide medical knowledge and relies on entity recognition and entity linking technology to standardize the medical expression in the main condition description text. Moreover, it uses the medical entity to generate knowledge subgraphs and obtain their semantic information and merges the semantic information of the subgraphs and patient condition description to obtain department confidence. When the confidence of the recommended department is low, multi-round interactive inquiry can supplement the patient’s symptom information, and finally, the recommended department is determined. The system can provide technical support for building a fast and accurate intelligent medical system to improve the efficiency of diagnosis and alleviate the shortage of medical resources.

    参考文献
    [1] 崔浩, 刘丰源. 智能导诊服务机器人的设计与实现. 计算机应用与软件, 2020, 37(7): 329–333. [doi: 10.3969/j.issn.1000-386x.2020.07.055
    [2] 郑姝雅. 面向在线问诊平台的精准导医模型构建研究[硕士学位论文]. 南京: 南京大学, 2020.
    [3] 陆康. 基于知识图谱的医疗导诊问答系统的设计与实现[硕士学位论文]. 武汉: 华中科技大学, 2020.
    [4] Liu DW, Ma ZY, Zhou YM, et al. Intelligent hospital guidance system based on multi-round conversation. 2019 IEEE International Conference on Bioinformatics and Biomedicine. San Diego: IEEE, 2019. 1540–1543.
    [5] Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications. Journal of the American Medical Informatics Association, 2010, 17(5): 507–513. [doi: 10.1136/jamia.2009.001560
    [6] Coden A, Savova G, Sominsky I, et al. Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. Journal of Biomedical Informatics, 2009, 42(5): 937–949. [doi: 10.1016/j.jbi.2008.12.005
    [7] Liu KX, Hu QC, Liu JW, et al. Named entity recognition in Chinese electronic medical records based on CRF. 2017 14th Web Information Systems and Applications Conference (WISA). Liuzhou: IEEE, 2017. 105–110.
    [8] Almgren S, Pavlov S, Mogren O. Named entity recognition in swedish health records with character-based deep bidirectional lstms. Proceedings of the 5th Workshop on Building and Evaluating Resources for Biomedical Text Mining. Osaka: ACL, 2016. 30–39.
    [9] Xu K, Zhou ZF, Hao TY, et al. A bidirectional LSTM and conditional random fields approach to medical named entity recognition. International Conference on Advanced Intelligent Systems and Informatics. Cham: Springer, 2017. 355–365.
    [10] Devlin J, Chang MW, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: ACL, 2019. 4171–4186.
    [11] 侯梦薇, 卫荣, 陆亮, 等. 知识图谱研究综述及其在医疗领域的应用. 计算机研究与发展, 2018, 55(12): 2585–2599. [doi: 10.7544/issn1000-1239.2018.20180623
    [12] 奥德玛, 杨云飞, 穗志方, 等. 中文医学知识图谱CMeKG构建初探. 中文信息学报, 2019, 33(10): 1–9. [doi: 10.3969/j.issn.1003-0077.2019.10.001
    [13] 昝红英, 韩杨超, 范亚鑫, 等. 中文症状知识库的建立与分析. 中文信息学报, 2020, 34(4): 30–37. [doi: 10.3969/j.issn.1003-0077.2020.04.004
    [14] 汤人杰, 杨巧节. 基于医疗知识图谱的智能辅助问诊模型研究. 中国数字医学, 2020, 15(10): 5–8. [doi: 10.3969/j.issn.1673-7571.2020.10.002
    [15] Wang Y, Zhang RC, Xu C, et al. The APVA-TURBO approach to question answering in knowledge base. Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe: ACL, 2018. 1998–2009.
    [16] Feng YL, Chen XY, Lin BY, et al. Scalable multi-hop relational reasoning for knowledge-aware question answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2020. 1295–1309.
    [17] Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2013. 2787–2795.
    [18] Socher R, Chen DQ, Manning CD, et al. Reasoning with neural tensor networks for knowledge base completion. Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2013. 926–934.
    [19] Nathani D, Chauhan J, Sharma C, et al. Learning attention-based embeddings for relation prediction in knowledge graphs. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence: ACL, 2019. 4710–4723.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

全威,马志柔,刘杰,叶丹,钟华.基于医疗知识图谱的交互式智能导诊系统.计算机系统应用,2021,30(12):55-62

复制
分享
文章指标
  • 点击次数:1283
  • 下载次数: 4026
  • HTML阅读次数: 4792
  • 引用次数: 0
历史
  • 收稿日期:2021-02-25
  • 最后修改日期:2021-03-19
  • 在线发布日期: 2021-12-10
文章二维码
您是第11117171位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号