Semantic Web Techniques for Clinical Topic Detection in Health Care

R. Raman; Kishore Anthuvan Sahayaraj; Mukesh Soni; Nihar Ranjan Nayak; Ramya Govindaraj; Nikhil Kumar Singh

doi:10.24423/cames.2024.493

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

DOI: http://dx.doi.org/10.24423/cames.2024.493

Abstract

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.

Keywords

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

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