Semantic Web Techniques for Clinical Topic Detection in Health Care

  • R. Raman Department of Electronics and Communication Engineering, Aditya College of Engineering, Surampalem, Andhra Pradesh, India
  • Kishore Anthuvan Sahayaraj Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Tamil Nadu, India
  • Mukesh Soni Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune, India
  • Nihar Ranjan Nayak Department of Computer Science & Engineering and Information Science, Presidency University, Bangaluru, India
  • Ramya Govindaraj School of Information Technology and Engineering, Vellore Institute of Technology, India
  • Nikhil Kumar Singh Department of Computer Science Engineering, Maulana Azad National Institute of Technology, Bhopal, India


The scope of this paper is that it investigates and proposes a new clustering method that takes into account the timing characteristics of frequently used feature words and the semantic similarity of microblog short texts as well as designing and implementing microblog topic detection and detection based on clustering results. The aim of the proposed research is to provide a new cluster overlap reduction method based on the divisions of semantic memberships to solve limited semantic expression and diversify short microblog contents. First, by defining the time-series frequent word set of the microblog text, a feature word selection method for hot topics is given; then, for the existence of initial clusters, according to the time-series recurring feature word set, to obtain the initial clustering of the microblog.


clinical text, frequent word set, feature selection, clustering, topic detection, time sequence, semantics,


1. A. Ejnioui, C.E. Otero, A.A. Qureshi, Formal semantics of interactions in sequence diagrams for embedded software, [in:] 2013 IEEE Conference on Open Systems (ICOS), 2–4 December, Kuching, Malaysia, 2013, pp. 106–111, doi: 10.1109/ICOS.2013.6735057.
2. J. Mansouri, B. Seddik, S. Gazzah, T. Chateau, Coarse localization using space-time and semantic-context representations of geo-referenced video sequences, [in:] 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 10–13 November, Orleans, France, 2015, pp. 355–359, doi: 10.1109/IPTA.2015.7367165.
3. L. Yao et al., HSD: Hybrid MARTE sequence diagram, [in:] 2015 IEEE International Conference on Software Quality, Reliability and Security, 3–5 August, Vancouver, BC, Canada, 2015, pp. 189–194, doi: 10.1109/QRS.2015.35.
4. Y. Fang et al., Salient object detection by spatiotemporal and semantic features in realtime video processing systems, IEEE Transactions on Industrial Electronics, 67(11): 9893–9903, 2020, doi: 10.1109/TIE.2019.2956418.
5. A. Shobanadevi et al., Internet of things-based data hiding scheme for wireless communication, Wireless Communications and Mobile Computing, 2022: Article ID 6997190, 8 pages, 2022, doi: 10.1155/2022/6997190.
6. Z. Xiang, S. Zhi-qing, ASM semantic modeling and checking for sequence diagram, [in:] 2009 Fifth International Conference on Natural Computation, 14–16 August, Tianjian, China, pp. 527–530, 2009, doi: 10.1109/ICNC.2009.218.
7. A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80: 23983–24004, 2021, doi: 10.1007/s11042-021-10840-0.
8. M. Soni, G. Dhiman, B.S. Rajput, R. Patel, N.K. Tejra, Energy-effective and secure data transfer scheme for mobile nodes in smart city applications, Wireless Personal Communications, 127: 2041–2061, 2021, doi: 10.1007/s11277-021-08767-8.
9. C. Jia, M.B. Carson, X. Wang, J. Yu, Concept decompositions for short text clustering by identifying word communities, Pattern Recognition, 76: 691–703, 2018, doi: 10.1016/j.patcog.2017.09.045.
10. A. Cioppa, M.V. Droogenbroeck, M. Braham, Real-time semantic background subtraction, [in:] 2020 IEEE International Conference on Image Processing (ICIP), 25–28 October, Abu Dhabi, UAE, pp. 3214–3218, 2020, doi: 10.1109/ICIP40778.2020.9190838.
11. T. Wang, K. Jia, M. Yao, Sequence matching with discriminative binary features for robust and fast light-rail localization at high frame rate, [in:] 2020 IEEE International Conference on Big Data (Big Data), 10–13 December, Atlanta, GA, USA, pp. 1266–1272, 2020, doi: 10.1109/BigData50022.2020.9378494.
12. N.H. Kirk, K. Ramírez-Amaro, E. Dean-León, M. Saveriano, G. Cheng, Online prediction of activities with structure: Exploiting contextual associations and sequences, [in:] 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), 3–5 November, Seoul, South Korea, pp. 744–749, 2015, doi: 10.1109/HUMANOIDS.2015.7363453.
13. Q. Li, B. Tian, M. Zhang, An event sequence based method for audio scene analysis, [in:] 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology, 28–30 October, Shenzen, China, pp. 255–259, 2011, doi: 10.1109/ICBNMT.2011.6155936.
14. S. Han, Z. Xi, Dynamic scene semantics SLAM based on semantic segmentation, IEEE Access, 8: 43563–43570, 2020, doi: 10.1109/ACCESS.2020.2977684.
15. C. Li, H. Xiao, K. Tateno, F. Tombari, N. Navab, G.D. Hager, Incremental scene understanding on dense SLAM, [in:] 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9–14 October, Daejeon, South Korea, pp. 574–581, 2016, doi: 10.1109/IROS.2016.7759111.
16. M. Siam, A. Kendall, M. Jagersand, Video class agnostic segmentation benchmark for autonomous driving, [in:] 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 20–25 June, on-line, pp. 2819–2828, 2021, doi: 10.1109/CVPRW53098.2021.00317.
17. J. Yang, F. Wang, J. Yang, Dynamic gesture recognition using LBRCN combined with attention mechanism, [in:] 2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT), 11–13 June, Changsha, China, pp. 466–469, 2021, doi: 10.1109/ISCIPT53667.2021.00100.
18. X. Li, Y. Tian, F. Zhang, S. Quan, Y. Xu, Object detection in the context of mobile augmented reality, [in:] 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 9–13 November, Porto de Galinhas, Brazil, pp. 156–163, 2020, doi: 10.1109/ISMAR50242.2020.00037.
19. C. Chakraborty, Performance analysis of compression techniques for chronic wound image transmission under smartphone-enabled tele-wound network, International Journal of E-Health and Medical Communications, 10(2): 1–20, 2019, doi: 10.4018/ijehmc.2019040101.
20. K.N. Mishra, C. Chakraborty, A novel approach toward enhancing the quality of life in smart cities using clouds and IoT-based technologies, [in:] Digital Twin Technologies and Smart Cities, Springer, pp. 19–35, 2019, doi: 10.1007/978-3-030-18732-3_2.
21. S.K. Narayanasamy, K. Srinivasan, Y.-C. Hu, S.K. Masilamani, K.-Y. Huang, A contemporary review on utilizing semantic web technologies in healthcare, virtual communities, and ontology-based information processing systems, Electronics, 11(3): 453, 2022, doi: 10.3390/electronics11030453.
22. A. Kishor, W. Jeberson, Diagnosis of heart disease using internet of things and machine learning algorithms, [in:] Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, Springer Singapore, vol. 203, pp. 691–702, 2021, doi: 10.1007/978-981-16-0733-2_49.
23. C. Friedrich, A. Lechler, A. Verl, A planning system for generating manipulation sequences for the automation of maintenance tasks, [in:] 2016 IEEE International Conference on Automation Science and Engineering (CASE), 21–25 August, Fort Worth, Texas, pp. 843–848, 2016, doi: 10.1109/COASE.2016.7743489.
May 29, 2024
How to Cite
RAMAN, R. et al. Semantic Web Techniques for Clinical Topic Detection in Health Care. Computer Assisted Methods in Engineering and Science, [S.l.], v. 31, n. 2, p. 139–155, may 2024. ISSN 2956-5839. Available at: <>. Date accessed: 18 july 2024. doi:
[CLOSED]Scientific Computing and Learning Analytics for Smart Healthcare Systems