Abstract
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.
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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.
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.
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).
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.
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.
JIANG L H, WANG W. Medical knowledge-base and medical knowledge acquirement [J]. Medical Information, 2006, 19(9): 1500–1502 (in Chinese).
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).
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).
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).
LU Y J. Research on text mining in online health community [D]. Shanghai, China: Shanghai Jiao Tong University, 2013 (in Chinese).
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).
OU Z H. Knowledge management and knowledge evaluation [J]. Document, Information & Knowledge, 2006 (3): 100–103 (in Chinese).
HE Y S, LI Y D. Knowledge management and medical knowledge management system [J]. Chinese Hospitals, 2001, 5(4): 51–53 (in Chinese).
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).
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.
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.
ALSHEHRI M G, JOURY A U. Quality, readability, and understandability of Internet-based information on cataract [J]. Health and Technology, 2019, 9: 791–795.
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.
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.
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).
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.
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).
WEN T X. Study on knowledge kmeasurement and knowledge evaluation [J]. Evaluation & Management, 2007, 5(1): 70–75 (in Chinese).
MCELROY M W. The new knowledge management [M]. Boston, USA: Butterworth-Heinemann, 2003: 3–32.
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).
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
BIANCANI S. Measuring the quality of edits to Wikipedia [C]//Proceedings of The International Symposium on Open Collaboration. Berlin, Germany: ACM, 2014: 1–3.
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.
SUZUKI Y. Quality assessment of Wikipedia articles using h-index [J]. Journal of Information Processing, 2015, 23(1): 22–30.
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.
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.
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).
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.
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).
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).
HARDALOVM, KOYCHEVI, NAKOVP. In search of credible news [C]//International Conference on Artificial Intelligence: Methodology, Systems, and Applications. Cham, Switzerland: Springer, 2016: 172–180.
SU J. Credibility measurement of network information based on domain knowledge graph [D]. Zhenjiang, China: Jiangsu University of Science and Technology, 2018 (in Chinese).
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).
ERTEL W. Introduction to artificial intelligence [M]. 2nd ed. Cham, Switzerland: Springer, 2017.
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).
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.
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the National Natural Science Foundation of China (Nos. 91646205 and 71421002), and the Fundamental Research Funds for the Central Universities of China (No. 16JCCS08)
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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). https://doi.org/10.1007/s12204-021-2266-8
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DOI: https://doi.org/10.1007/s12204-021-2266-8