Content-Based Knowledge Quality Assessment and Its Application in Health Management System


The growing demand for health management puts forward high requirements for the quality of health knowledge. A content-based method is proposed to address the current demand for high-quality health knowledge, which evaluates the quality including the certainty, accuracy, and operability of different types of knowledge from the perspectives of authority, precision, and information entropy. Herein, the health knowledge of myocardial infarction is used as an example, and knowledge is first classified into different types and then evaluated. This method is applied to knowledge in the existing health management system and it can support knowledge screening and comparison under the cold start condition. Compared with the current evaluation methods based on knowledge use behavior and utility, the new evaluation method provides a reference for evaluation when the knowledge is first used. The screening of high quality knowledge can help the subsequent application of knowledge and improve user’s compliance. Concurrently, the arrangement of myocardial infarction knowledge can also provide a knowledge reference for patients’ daily health management.

This is a preview of subscription content, access via your institution.


  1. [1]

    BARRETT C. Pew Internet and American life project [M]//GELLMAN M D, TURNER J R. Encyclopedia of behavioral medicine. New York, USA: Springer, 2013: 1464–1465.

    Google Scholar 

  2. [2]

    DIAZ J A, GRIFFITH R A, NG J J, et al. Patients’ use of the Internet for medical information [J]. Journal of General Internal Medicine, 2002, 17(3): 180–185.

    Article  Google Scholar 

  3. [3]

    WANG B. Research on credibility of medical information based on web analysis algorithm [D]. Mianyang, China: Southwest University of Science and Technology, 2017 (in Chinese).

    Google Scholar 

  4. [4]

    EYSENBACH G, POWELL J, KUSS O, et al. Empirical studies assessing the quality of health information for consumers on the World Wide Web: A systematic review [J]. Journal of the American Medical Association, 2002, 287(20): 2691–2700.

    Article  Google Scholar 

  5. [5]

    FAHY E, HARDIKAR R, FOX A, et al. Quality of patient health information on the Internet: Reviewing a complex and evolving landscape [J]. Australasian Medical Journal, 2014, 7(1): 24–28.

    Article  Google Scholar 

  6. [6]

    JIANG L H, WANG W. Medical knowledge-base and medical knowledge acquirement [J]. Medical Information, 2006, 19(9): 1500–1502 (in Chinese).

    Google Scholar 

  7. [7]

    BYAMBASUREN O, YANG Y F, SUI Z F, et al. Preliminary study on the construction of Chinese medical knowledge graph [J]. Journal of Chinese Information Processing, 2019, 33(10): 1–9 (in Chinese).

    Google Scholar 

  8. [8]

    YUAN K Q, DENG Y, CHEN D Y, et al. Construction techniques and research development of medical knowledge graph [J]. Application Research of Computers, 2018, 35(7): 1929–1936 (in Chinese).

    Google Scholar 

  9. [9]

    ZHENG Y M, ZHAI J, HU X L, et al. Intelligent Q&A and drug recommendation system based on TCM knowledge map [J]. Electronic Technology & Software Engineering, 2019 (20): 134–135 (in Chinese).

  10. [10]

    LU Y J. Research on text mining in online health community [D]. Shanghai, China: Shanghai Jiao Tong University, 2013 (in Chinese).

    Google Scholar 

  11. [11]

    QU A Z, ZHUANG T G. The research of data mining and knowledge discovery in computer aided medical diagnosing system [J]. Foreign Medical Sciences Biomedical Engineering, 2002, 25(3): 97–103 (in Chinese).

    Google Scholar 

  12. [12]

    OU Z H. Knowledge management and knowledge evaluation [J]. Document, Information & Knowledge, 2006 (3): 100–103 (in Chinese).

  13. [13]

    HE Y S, LI Y D. Knowledge management and medical knowledge management system [J]. Chinese Hospitals, 2001, 5(4): 51–53 (in Chinese).

    Google Scholar 

  14. [14]

    HOU X R, CHEN J Y, ZHAO W L. Quality analysis of public medical and health information on the internet [J]. Chinese Journal of Health Informatics and Management, 2014, 11(1): 38–42 (in Chinese).

    Google Scholar 

  15. [15]

    ROTHROCK S G, ROTHROCK A N, SWETLAND S B, et al. Quality, trustworthiness, readability, and accuracy of medical information regarding common pediatric emergency medicine-related complaints on the Web [J]. The Journal of Emergency Medicine, 2019, 57(4): 469–477.

    Article  Google Scholar 

  16. [16]

    CHAROW R, SNOW M, FATHIMA S, et al. Evaluation of the scope, quality, and health literacy demand of Internet-based anal cancer information [J]. Journal of the Medical Library Association, 2019, 107(4): 527–537.

    Article  Google Scholar 

  17. [17]

    ALSHEHRI M G, JOURY A U. Quality, readability, and understandability of Internet-based information on cataract [J]. Health and Technology, 2019, 9: 791–795.

    Article  Google Scholar 

  18. [18]

    RANADE A S, BELTHUR M V, OKA G A, et al. YouTube as an information source for clubfoot: A quality analysis of video content [J]. Journal of Pediatric Orthopedics B, 2020, 29(4): 375–378.

    Article  Google Scholar 

  19. [19]

    XIA Z F, GU K, WANG S Q, et al. Toward accurate quality estimation of screen content pictures with very sparse reference information [J]. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2251–2261.

    Article  Google Scholar 

  20. [20]

    LE C Y, GU X J. Enterprise knowledge automatic evaluation method based on user bahavior analysis [J]. Computer Integrated Manufacturing Systems, 2015, 21(5): 1368–1374 (in Chinese).

    Google Scholar 

  21. [21]

    FIRESTONE J M, MCELROY M W. Introduction: What is the new knowledge management (TNKM), and what are its key issues? [M]//Key issues in the new knowledge management. Boston, USA: Butterworth-Heinemann, 2003.

    Google Scholar 

  22. [22]

    LE C Y, XU F Y, GU X J, et al. Evaluation model and algorithm for knowledge contribution of enterprise staff [J]. Computer Integrated Manufacturing Systems, 2011, 17(3): 662–671 (in Chinese).

    Google Scholar 

  23. [23]

    WEN T X. Study on knowledge kmeasurement and knowledge evaluation [J]. Evaluation & Management, 2007, 5(1): 70–75 (in Chinese).

    Google Scholar 

  24. [24]

    MCELROY M W. The new knowledge management [M]. Boston, USA: Butterworth-Heinemann, 2003: 3–32.

    Google Scholar 

  25. [25]

    DENG S L, ZHAO H P. Quality evaluation of foreign network health information: A review of indicators, tools and results [J]. Information and Documentation Services, 2017 (1): 67–74 (in Chinese).

  26. [26]

    MARCINKOW A, PARKHOMCHIK P, SCHMODE A, et al. The quality of information on combined oral contraceptives available on the Internet [J]. Journal of Obstetrics and Gynaecology Canada, 2019, 41(11): 1599–1607.

    Article  Google Scholar 

  27. [27]

    BAI X Y, ZHANG Y W, LI J, et al. Online information on Crohn’s disease in Chinese: An evaluation of its quality and readability [J]. Journal of Digestive Diseases, 2019, 20: 596–601.

    Article  Google Scholar 

  28. [28]

    RAPTIS D A, SINANYAN M, GHANI S, et al. Quality assessment of patient information on the management of gallstone disease in the internet: A systematic analysis using the modified ensuring quality information for patients tool [J]. HPB: The Official Journal of the International Hepato Pancreato Biliary Association, 2019, 21(12): 1632–1640.

    Article  Google Scholar 

  29. [29]

    TAVARE A N, ALSAFI A, HAMADY M S. Analysis of the quality of information obtained about uterine artery embolization from the Internet [J]. Cardiovascular and Interventional Radiology, 2012, 35: 1355–1362.

    Article  Google Scholar 

  30. [30]

    BURKE E, HARKINS P, SAEED M, et al. “Dr. Google” will see you now—assessing the quality of information on oesophageal cancer on the internet [J]. Journal of Gastrointestinal Surgery, 2020, 24: 2466–2470.

    Article  Google Scholar 

  31. [31]

    DENG S L, ZHAO H P. Research on the standard framework of the quality and the content evaluation of online health information from users’ perspective [J]. Library and Information Service, 2017, 61(21): 30–39 (in Chinese).

    Google Scholar 

  32. [32]

    BLUMENSTOCK J E. Size matters: Word count as a measure of quality on Wikipedia [C]//Proceedings of the 17th International Conference on World Wide Web. Beijing, China: ACM, 2008: 1095–1096.

    Google Scholar 

  33. [33]

    WILKINSON D, HUBERMAN B. Cooperation and quality in Wikipedia [C]//Proceedings of the 2007 International Symposium on Wikis. New York, USA: ACM, 2007: 157–164.

    Google Scholar 

  34. [34]

    ADLER B T, DE ALFARO L. A content-driven reputation system for the Wikipedia [C]//Proceedings of the 16th International Conference on World Wide Web. Banff, Alberta, Canada: ACM, 2007: 261–270.

    Google Scholar 

  35. [35]

    HU M Q, LIM E P, SUN A X, et al. Measuring article quality in Wikipedia: Models and evaluation [C]//Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management. Lisbon, Portugal: ACM, 2007: 243–252.

    Google Scholar 

  36. [36]

    WÖHNER T, PETERS R. Assessing the quality of Wikipedia articles with lifecycle based metrics [C]//Proceedings of the 5th International Symposium on Wikis and Open Collaboration. Orlando, Florida, USA: ACM, 2009: 1–10.

    Google Scholar 

  37. [37]

    BIANCANI S. Measuring the quality of edits to Wikipedia [C]//Proceedings of The International Symposium on Open Collaboration. Berlin, Germany: ACM, 2014: 1–3.

    Google Scholar 

  38. [38]

    LI X Y, TANG J T, WANG T, et al. Automatically assessing Wikipedia article quality by exploiting article-editor networks [C]//Proceedings of the 37th European Conference on IR Research. Vienna, Austria: Springer, 2015: 574–580.

    Google Scholar 

  39. [39]

    SUZUKI Y. Quality assessment of Wikipedia articles using h-index [J]. Journal of Information Processing, 2015, 23(1): 22–30.

    Article  Google Scholar 

  40. [40]

    SUZUKI Y, YOSHIKAWA M. Assessing quality score of Wikipedia article using mutual evaluation of editors and texts [C]//Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. San Francisco, California, USA: ACM, 2013: 1727–1732.

    Google Scholar 

  41. [41]

    DE LA ROBERTIE B, PITARCH Y, TESTE O. Measuring article quality in Wikipedia using the collaboration network [C]//Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Paris, France: ACM, 2015: 464–471.

    Google Scholar 

  42. [42]

    WU L W, RAO Y, FAN X B, et al. A study on the credibility of information spreaded on social networks [J]. Journal of Chinese Information Processing, 2018, 32(2): 1–11 (in Chinese).

    Google Scholar 

  43. [43]

    PETERS K, MARUSTER L, JORNA R J. Knowledge claim evaluation: A fundamental issue for knowledge management [J]. Journal of Knowledge Management, 2010, 14(2): 243–257.

    Article  Google Scholar 

  44. [44]

    QIU Q L, DAI F, DONG J F, et al. Research on explicit-knowledge evaluation system of the enterprises’ knowledge pool [J]. Mechanical Engineer, 2012 (7): 1–3 (in Chinese).

  45. [45]

    LI X S, ZHANG L L, ZHU Z X. Intelligent evaluation methods for knowledge acquired through data mining [J]. Science Research Management, 2010, 31(Sup 1): 32–38 (in Chinese).

    Google Scholar 

  46. [46]

    HARDALOVM, KOYCHEVI, NAKOVP. In search of credible news [C]//International Conference on Artificial Intelligence: Methodology, Systems, and Applications. Cham, Switzerland: Springer, 2016: 172–180.

    Google Scholar 

  47. [47]

    SU J. Credibility measurement of network information based on domain knowledge graph [D]. Zhenjiang, China: Jiangsu University of Science and Technology, 2018 (in Chinese).

    Google Scholar 

  48. [48]

    CUI W. Research and realization of expert system for intelligent manufacturing based on uncertainty and fuzzy reasoning [D]. Tianjin, China: Tianjin University, 2014 (in Chinese).

    Google Scholar 

  49. [49]

    ERTEL W. Introduction to artificial intelligence [M]. 2nd ed. Cham, Switzerland: Springer, 2017.

    Google Scholar 

  50. [50]

    YANG R X, MAO Y L. Discussion on naming recognition method of Chinese scientific research Institutions catering to knowledge evaluation [J]. Journal of Intelligence, 2015, 34(7): 179–183 (in Chinese).

    Google Scholar 

  51. [51]

    ZHANG W Q, XIANG Y D, LIU X H, et al. Domain ontology development of knowledge base in cardiovascular personalized health management [J]. Journal of Management Analytics, 2019, 6(4): 420–455.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Pengzhu Zhang 张朋柱.

Additional information

Foundation item

the National Natural Science Foundation of China (Nos. 91646205 and 71421002), and the Fundamental Research Funds for the Central Universities of China (No. 16JCCS08)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiang, Y., Zhang, P. & Wu, S. Content-Based Knowledge Quality Assessment and Its Application in Health Management System. J. Shanghai Jiaotong Univ. (Sci.) 26, 116–128 (2021).

Download citation

Key words

  • knowledge quality assessment
  • knowledge management
  • health management system
  • myocardial infarction

CLC number

  • C 931.6

Document code

  • A