Online Healthcare Privacy Disclosure User Group Profile Modeling Based on Multimodal Fusion
DOI:
https://doi.org/10.15837/ijccc.2022.5.4696Keywords:
multimodal fusion technology, group profiling, privacy disclosure, online healthcare platform, personalized healthcare servicesAbstract
With the spread of COVID-19, online healthcare is rapidly evolving to assist the public with health, reduce exposure and avoid the risk of cross-infection. Online healthcare platform requires more information from patients than offline, and insufficient or incorrect information may delay or even mislead treatment. Therefore, it is valuable to predict users’ privacy disclosure behaviors while fully protecting their information, which can provide healthcare services for users accurately and realize a personalized online healthcare environment. Compared with the traditional static online healthcare platform user privacy disclosure behavior influence factor analysis, this paper uses multimodal fusion and group profile technology to build a user privacy disclosure model and lay the foundation for personalized online healthcare services. This paper proposes a cross-modal fusion modeling approach to address the problem that the information of each modality cannot be fully utilized in the current online healthcare privacy disclosure modeling. A multimodal user profile approach is used to construct personal and group profiles, and the privacy disclosure behavioral characteristics reflected by both are integrated to realize accurate personalized services for online healthcare. The case study shows that compared with the static unimodal privacy disclosure model, the accuracy of our method gains significant improvement, which is helpful for precision healthcare services and online healthcare platform development.
References
Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions[J]. BMC health services research, 2010, 10(1): 1-17.
https://doi.org/10.1186/1472-6963-10-231
Sun S, Zhang J, Zhu Y, et al. Exploring users' willingness to disclose personal information in online healthcare communities: The role of satisfaction[J]. Technological Forecasting and Social Change, 2022, 178: 121596.
https://doi.org/10.1016/j.techfore.2022.121596
Kobrinskii B A, Grigoriev O G, Molodchenkov A I, et al. Artificial intelligence technologies application for personal health management[J]. IFAC-PapersOnLine, 2019, 52(25): 70-74.
https://doi.org/10.1016/j.ifacol.2019.12.448
Dai Q Y, Hong X B, Cai J, et al. Deep Learning Based Recommendation Algorithm in Online Medical Platform[C]//International Conference on Brain Inspired Cognitive Systems. Springer, Cham, 2018: 34-43.
https://doi.org/10.1007/978-3-030-00563-4_4
Dhiman G, Juneja S, Mohafez H, et al. Federated learning approach to protect healthcare data over big data scenario[J]. Sustainability, 2022, 14(5): 2500.
https://doi.org/10.3390/su14052500
Edemekong P F, Annamaraju P, Haydel M J. Health insurance portability and accountability act[J]. 2018.
Lye C T, Forman H P, Gao R, et al. Assessment of US hospital compliance with regulations for patients' requests for medical records[J]. JAMA network open, 2018, 1(6): e183014-e183014.
https://doi.org/10.1001/jamanetworkopen.2018.3014
Zhang X, Liu S, Chen X, et al. Health information privacy concerns, antecedents, and information disclosure intention in online health communities[J]. Information & Management, 2018, 55(4): 482-493.
https://doi.org/10.1016/j.im.2017.11.003
Bansal G, Gefen D. The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online[J]. Decision support systems, 2010, 49(2): 138- 150.
https://doi.org/10.1016/j.dss.2010.01.010
Wang, Yuchao, & Sun, Y. Q. The influence of service and reciprocity norm on self-disclosure intention in virtual health community[J]. Intelligence Science, 36(5), 149-157.
Zhang, Yue, & Zhu, Qing-Hua. Review on foreign study of information privacy[J]. Library and Information Service, 58(13), 140-148.
Wang T, Duong T D, Chen C C. Intention to disclose personal information via mobile applications: A privacy calculus perspective[J]. International journal of information management, 2016, 36(4): 531-542.
https://doi.org/10.1016/j.ijinfomgt.2016.03.003
Sapuppo A. Privacy analysis in mobile social networks: the influential factors for disclosure of personal data[J]. International Journal of Wireless and Mobile Computing, 2012, 5(4): 315-326.
https://doi.org/10.1504/IJWMC.2012.051517
Wang L, Yan J, Lin J, et al. Let the users tell the truth: Self-disclosure intention and self-disclosure honesty in mobile social networking[J]. International Journal of Information Management, 2017, 37(1): 1428-1440.
https://doi.org/10.1016/j.ijinfomgt.2016.10.006
Papadopoulou P, Pelet J E. Trust and privacy in the shift from e-commerce to m-commerce: A comparative approach[C]//Conference on e-Business, e-Services and e-Society. Springer, Berlin, Heidelberg, 2013: 50-60.
https://doi.org/10.1007/978-3-642-37437-1_5
Bergström A. Online privacy concerns: A broad approach to understanding the concerns of different groups for different uses[J]. Computers in Human Behavior, 2015, 53: 419-426.
https://doi.org/10.1016/j.chb.2015.07.025
Keith M, Thompson S, Hale J, et al. Examining the rationality of location data disclosure through mobile devices[J]. 2012.
Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.
https://doi.org/10.24963/ijcai.2017/435
Wei H, Zhang F, Yuan N J, et al. Beyond the words: Predicting user personality from heterogeneous information[C]//Proceedings of the tenth ACM international conference on web search and data mining. 2017: 305-314.
https://doi.org/10.1145/3018661.3018717
Wöllmer M, Weninger F, Knaup T, et al. Youtube movie reviews: Sentiment analysis in an audio-visual context[J]. IEEE Intelligent Systems, 2013, 28(3): 46-53.
https://doi.org/10.1109/MIS.2013.34
Gu Y, Yang K, Fu S, et al. Hybrid attention based multimodal network for spoken language classification[ C]//Proceedings of the Conference. association for Computational Linguistics. meeting. NIH Public Access, 2018, 2018: 2379.
Gu Y, Chen S, Marsic I. Deep mul timodal learning for emotion recognition in spoken language[ C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018: 5079-5083.
https://doi.org/10.1109/ICASSP.2018.8462440
Liu, W., & Wu, D.J. Research on factors influencing user privacy disclosure behavior of online healthcare platforms[J]. Journal of Medical Informatics, 42(6), 16-23.
Zhang Z, Feng X, Qian T. User profiling based on multimodal fusion technology[J]. Beijing Da Xue Xue Bao, 2020, 56(1): 105-111.
Salton G, Wong A, Yang C S. A vector space model for automatic indexing[J]. Communications of the ACM, 1975, 18(11): 613-620.
https://doi.org/10.1145/361219.361220
Hartigan J A, Wong M A. Algorithm AS 136: A k-means clustering algorithm[J]. Journal of the royal statistical society. series c (applied statistics), 1979, 28(1): 100-108.
Additional Files
Published
Issue
Section
License
Copyright (c) 2022 Yong Wang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
