[Purpose/Significance] In view of the low satisfaction of online doctor recommendation, this paper explores how to combine information technology and user cognition to improve the effect of the doctor recommendation system, which helps to optimize the user experience of online “Ask the Doctor” platform. [Method/Process] First, we established a doctor recommender prototype system based on the relevance theory and the NLP method based on the information of 1500 doctors and more than 780 thousands user questions; Then, did a qualitative study to analyze user’s thoughts in the process of using the recommender based on the sense-making; Finally, we optimized the recommender though considering the users’ perspectives. [Result/Conclusion] Word2Vec model has the best effect in the doctor recommendation task, which was up to 88% doctors in TOP10 doctor candidates are able to answer user questions. The user experiment results show that most users attach great importance to the doctor’s department and areas of expertise while similar questions answered by doctors. When judging the similarity of questions, users mainly pay attention to the medical terms, and avoid the irrelevant medical keywords. Based on these, two model optimizations were identified, including (1) a function of predicting departments was incorporated into the system, and doctors belonging to these departments were ranked forward, (2) a healthcare wordlist was built and higher weights were given to these words when calculating text similarity. Results show that these two methods improved the accuracy of the doctor recommender system, which indicates that the integration of the AI-related algorithms and the user’s thoughts can be well implemented.
Wang Ruojia
,
Wang Jimin
. Research on Doctor Recommendation of Online “Ask the Doctor” Platforms Based on the Perspective of Users Recognition[J]. Library and Information Service, 2023
, 67(10)
: 128
-138
.
DOI: 10.13266/j.issn.0252-3116.2023.10.013
[1] 中华人民共和国中央人民政府. 国务院关于积极推进"互联网+"行动的指导意见[EB/OL].[2022-11-21]. http://www.gov.cn/zhengce/content/2015-07/04/content_10002.htm.
[2] 中华人民共和国中央人民政府. 国务院办公厅关于促进" 互联网+ 医疗健康" 发展的意见[EB/OL].[2022-11-21]. http://www.gov.cn/zhengce/content/2018-04/28/content_5286645.htm.
[3] 易观. 中国互联网医疗年度分析2020[EB/OL].[2022-11-21]. http://www.199it.com/archives/1072988.html.
[4] 肖敏. 基于大数据的问诊推荐系统的研究与实现[D]. 西安:西北大学, 2018.
[5] 刘彭. 基于组合模型的医生推荐系统研究与实现[D]. 上海:东华大学, 2013.
[6] GONG J B, WANG L L, SUN S T, et al. iBole:a hybrid multilayer architecture for doctor recommendation in medical social networks[J]. Journal of computer science and technology, 2015, 30(5):1073-1081.
[7] SANAEIFAR A, FARAAHI A, TARA M. SEPHYRES 1:a physician recommender system based on semantic pain descriptors and multifaceted reasoning[J]. International journal of collaborative research on internal medicine & public health, 2018, 10(1):735-746.
[8] LEE H J, KIM H S. eHealth recommendation service system using ontology and case-based reasoning[C]//2015 IEEE international conference on smart city/socialcom/sustaincom (smartcity). New York:IEEE, 2015:1108-1113.
[9] 倪秀丽. 基于Labeled-LDA模型的在线医疗专家推荐研究[D]. 杭州:浙江大学, 2018.
[10] 刘通. 基于在线咨询记录的医生自动匹配算法应用研究[J]. 情报理论与实践, 2018, 41(6):143-148.
[11] NARDUCCI F, MUSTO C, POLIGNANO M, et al. A recommender system for connecting patients to the right doctors in the healthnet social network[C]//Proceedings of the 24th international conference on World Wide Web. New York:ACM, 2015:81-82.
[12] 刁必颂. 基于在线患者咨询数据的在线医生推荐系统研究[D]. 北京:北京理工大学, 2016.
[13] 熊回香, 李晓敏, 李建玲. 基于医患交互数据的在线医生推荐研究[J]. 情报理论与实践, 2020, 43(8):159-166.
[14] GUNNING D, STEFIK M, CHOI J, et al. XAI-explainable artificial intelligence[J]. Science robotics, 2019, 4(37):eaay7120.
[15] RUNDO L, PIRRONE R, VITABILE S, et al. Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine[J]. Journal of biomedical informatics, 2020, 108:103479.
[16] ASELMAA A, HERK M V, LAPRIE A, et al. Using a contextualized sensemaking model for interaction design:a case study of tumor contouring[J]. Journal of biomedical informatics, 2017, 65:145-158.
[17] MARKONIS D, HOLZER M, BAROZ F, et al. User-oriented evaluation of a medical image retrieval system for radiologists[J]. International journal of medical informatics, 2015, 84(10):774-783.
[18] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. arXiv preprint arXiv:1301.3781, 2013.
[19] LE Q V, MIKOLOV T. Distributed representations of sentences and documents[C]//Proceedings of the 31st international conference on international conference on machine learning. New York:ACM, 2014:1188-1196.
[20] 朱利, 岳爱珍. 健康问题和医生匹配机制的研究[J]. 西安交通大学学报, 2014(12):63-68, 145.
[21] 北京春雨天下软件有限公司. 春雨医生[EB/OL].[2022-11- 21]. https://www.chunyuyisheng.com/.
[22] 易观. 中国 移动 问诊 白皮 书2018[EB/OL].[2022-11-21]. https://www.analysys.cn/article/analysis/detail/1001147.
[23] 王若佳, 张璐, 王继民. 基于机器学习的在线问诊平台智能分诊研究[J]. 数据分析与知识发现, 2019, 3(9):88-97.
[24] SARACEVIC T. Relevance:a review of the literature and a framework for thinking on the notion in information science. part II:nature and manifestations of relevance[J]. Journal of the American Society for Information Science and Technology, 2007, 58(13):1915-1933.
[25] BARRY C L. User-defined relevance criteria:an exploratory study[J]. Journal of the American Society for Information Science, 1994, 45(3):149-159.
[26] ALBASSAM S A A, RUTHVEN I. Users' relevance criteria for video in leisure contexts[J]. Journal of documentation, 2018, 74(1):62-79.
[27] PIAN W, KHOO C S, CHANG Y K. The criteria people use in relevance decisions on health information:an analysis of user eye movements when browsing a health discussion forum[J]. Journal of medical internet research, 2016, 18(6):e136.
[28] KIM Y W, PARK S J. User-based relevance and irrelevance criteria during the task pursuing of middle school students[J]. Journal of the Korean Society for Library and Information Science, 2014, 48(3):55-70.
[29] REES A M, SCHULTZ D G. A field experimental approach to the study of relevance assessments in relation to document searching:final report:NSF contract no. C-423[R]. Cleveland:Case Western Reserve University, 1967:79-176.
[30] BALATSOUKAS P, RUTHVEN I. An eye-tracking approach to the analysis of relevance judgments on the Web:the case of google search engine[J]. Journal of the American Society for Information Science and Technology, 2012, 63(9):1728-1746.
