融合知识图谱和深度学习方法的问诊推荐系统

doi:10.3778/j.issn.1673-9418.2101029

摘要/Abstract

摘要：

近年来，随着互联网的普及和大数据分析等技术的发展，人们对移动医疗服务的需求越来越迫切，具体表现为根据症状确定自己患有的疾病以及根据疾病选择服务质量较好的医院及医生。为了解决上述问题，基于知识图谱和深度学习技术设计并实现了一种问诊推荐系统。基于互联网开放的医疗数据，构建了“疾病-症状”知识图谱，帮助用户根据症状自查，并以知识图谱嵌入模型训练知识图谱中实体的嵌入向量表示，根据向量的欧式距离相似度选取最相近的疾病实体丰富推荐选项，两者结合实现疾病诊断服务。同时，基于社交媒体的评论数据，结合现有的医疗服务质量评价指标，使用了深度学习的分析方法，自动给出医生的服务质量多维度的评分，为用户提供医生医院推荐服务。最后，通过构建测试集以及设计调查问卷等方式，验证了疾病诊断服务和医生医院推荐服务的准确率分别达到了74.00%和90.91%。

关键词: 医疗知识图谱, 评论情感分析, 深度学习, 推荐系统

Abstract:

In recent years, with the popularization of Internet and technologies like big data analysis, the demand for mobile medical services has become more and more urgent, which mainly focuses on ascertaining their diseases based on symptoms and further choosing hospitals and doctors with good service quality based on diseases. In order to tackle problems above, this paper designs and implements a recommendation system for medical consultation based on knowledge graph and deep learning. Using the open data on Internet, a “disease-symptom” knowledge graph is constructed. Once given symptom description, a disease candidate set is built to help user self-diagnose. To improve the accuracy of disease diagnosis, a vector representation of entities in the knowledge graph is trained by a knowledge graph embedding model. Then the disease candidate set is expanded by selecting disease entity with the shortest Euclidean distance with diseases in the set. Combining the two above, disease diagnosis service is provided. To recommend hospitals and doctors, given open media data, this paper uses a deep learning model and combines it with existing quality evaluation indicators for medical service to achieve scoring for doctors?? multi-dimensional service quality automatically. Finally, this paper verifies the accuracy of the disease diagnosis service and the doctor recommendation service by constructing test sets and designing questionnaires, which reach 74.00% and 90.91%, respectively.

Key words: medical knowledge graph, comment sentiment analysis, deep learning, recommendation system

武家伟, 孙艳春. 融合知识图谱和深度学习方法的问诊推荐系统[J]. 计算机科学与探索, 2021, 15(8): 1432-1440.

WU Jiawei, SUN Yanchun. Recommendation System for Medical Consultation Integrating Knowledge Graph and Deep Learning Methods[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(8): 1432-1440.

参考文献

[1] DE S K, BISWAS R, ROY A R. An application of intuitioni-stic fuzzy sets in medical diagnosis[J]. Fuzzy Sets and Sys-tems, 2001, 117(2): 209-213.
[2] YE J. Improved cosine similarity measures of simplified neu-trosophic sets for medical diagnoses[J]. Artificial intellig-ence in Medicine, 2015, 63(3): 171-179.
[3] YE S, YE J. Dice similarity measure between single valued neutrosophic multisets and its application in medical diag-nosis[J]. Neutrosophic Sets and Systems, 2014, 6: 48-53.
[4] YE S, FU J, YE J. Medical diagnosis using distance-based similarity measures of single valued neutrosophic multisets[J]. Neutrosophic Sets and Systems, 2015, 7: 47-52.
[5] DAVIS D A, CHAWLA N V, BLUMM N, et al. Predicting individual disease risk based on medical history[C]//Pro-ceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley, Oct 26-30, 2008. New York: ACM, 2008: 769-778.
[6] THANH N D, ALI M. A novel clustering algorithm in a neutrosophic recommender system for medical diagnosis[J]. Cognitive Computation, 2017, 9(4): 526-544.
[7] ALI M, THANH N D, VAN MINH N. A neutrosophic reco-mmender system for medical diagnosis based on algebraic neutrosophic measures[J]. Applied Soft Computing, 2018, 71: 1054-1071.
[8] RAVAL D, BHATT D, KUMHAR M K, et al. Medical dia-gnosis system using machine learning[J]. International Jour-nal of Computer Science & Communication, 2016, 7(1): 177-182.
[9] ACHARYA U R, FUJITA H, OH S L, et al. Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals[J]. Information Sciences, 2017, 415: 190-198.
[10] ESTEVA A, KUPREL B, NOVOA R A, et al. Dermato-logist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118.
[11] BAKATOR M, RADOSAV D. Deep learning and medical diagnosis: a review of literature[J]. Multimodal Technologies and Interaction, 2018, 2(3): 47.
[12] LIU Q, LI Y, DUAN H, et al. Konwledge graph construction techniques[J]. Journal of Computer Research and Develop-ment, 2016, 53(3): 582-600.
刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
[13] ROTMENSCH M, HALPERN Y, TLIMAT A, et al. Learn-ing a health knowledge graph from electronic medical re-cords[J]. Scientific Reports, 2017, 7(1): 5994.
[14] WANG M, LIU M, LIU J, et al. Safe medicine recommend-ation via medical knowledge graph embedding[J]. arXiv:1710.05980, 2017.
[15] LI L, WANG P, YAN J, et al. Real-world data medical know-ledge graph: construction and applications[J]. Artificial Intelligence in Medicine, 2020, 103: 101817.
[16] HOU M W, WEI R, LU L, et al. Research review of know-ledge graph and its application in medical domain[J]. Jour-nal of Computer Research and Development, 2018, 55(12): 5-17.
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[J]. 计算机研究与发展, 2018, 55(12): 5-17.
[17] PANG B, LEE L. Opinion mining and sentiment analysis[M]. [S.l.]: Now Publishers Inc, 2008.
[18] QIU B, ZHAO K, MITRA P, et al. Get online support, feel better—sentiment analysis and dynamics in an online cancer survivor community[C]//Proceedings of the 2011 IEEE 3rd International Conference on Privacy, Security, Risk and Trust, Boston, Oct 9-11, 2011. Washington: IEEE Computer Society, 2011: 274-281.
[19] BIYANI P, CARAGEA C, MITRA P, et al. Co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community[C]//Proceedings of the 2013 IEEE/ACM International Con-ference on Advances in Social Networks Analysis & Min-ing. Piscataway: IEEE, 2013: 413-417.
[20] SALAS-ZáRATE M D P, MEDINA-MOREIRA J, LAGOS-ORTIZ K, et al. Sentiment analysis on Tweets about diabetes: an aspect-level approach[J]. Computational & Mathematical Methods in Medicine, 2017: 5140631.
[21] HAO H J, ZHANG K P. The voice of Chinese health con-sumers: a text mining approach to Web-based physician re-views[J]. Journal of Medical Internet Research, 2016, 18(5): e108.
[22] PARASURAMAN A, ZEITHAML V A, BERRY L L. SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality[J]. Journal of Retailing, 1988, 64(1): 12-40.
[23] BABAKUS E, MANGOLD W G. Adapting the SERVQUAL scale to hospital services: an empirical investigation[J]. Health Services Research, 1992, 26(6): 767-786.
[24] DEVLIN J, CHANG M, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understand-ing[J]. arXiv:1810.04805, 2018.
[25] PETERS M E, NEUMANN M, IYYER M, et al. Deep con-textualized word representations[J]. arXiv:1802.05365, 2018.
[26] JI G, HE S, XU L, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Lang-uage Processing, Beijing, Jul 26-31, 2015. Stroudsburg: ACL, 2015: 687-696.
[27] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating embeddings for modeling multi-relational data[C]//Proceedings of the 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Dec 5-8, 2013: 2787-2795.
[28] WANG Z, ZHANG J W, FENG J L, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, Jul 27-31, 2014. Menlo Park: AAAI, 2014: 1112-1119.
[29] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, Jan 25-30, 2015. Menlo Park: AAAI, 2015: 2181-2187.
[30] HAN X, CAO S L, LV X, et al. Openke: an open toolkit for knowledge embedding[C]//Proceedings of the 2018 Confer-ence on Empirical Methods in Natural Language Process-ing: System Demonstrations, Brussels, Oct 31-Nov 4, 2018. Stroudsburg: ACL, 2018: 139-144.
[31] BOPP K D. How patients evaluate the quality of ambula-tory medical encounters: a marketing perspective[J]. The Journal of Health Care Marketing, 1990, 10(1): 6-15.
[32] BROWN S W, SWARTZ T A. A gap analysis of professional service quality[J]. Journal of Marketing, 1989, 53(2): 92-98.