Harnessing Textual Mining for Web-Based Health Monitoring in Healthcare: A Comprehensive Review

Abstract

Web-based health monitoring is useful to improve timely transmission of health-related information of remotely located patients to concern people. This system comprises a broad range of technologies and methodologies that uses the internet and connected devices to monitor and manage health related data remotely. The rapid growth of digital health-related content has created unprecedented opportunities to apply technique like text mining for monitoring and analyzing healthcare trends.  Textual mining is nothing but an advanced natural language processing (NLP) approach which enables the extraction of actionable insights from unstructured web-based data. Such data may be collected from social media posts, health forums, and online articles. (Heterogeneous data). This paper provides a comprehensive review of how textual mining is applied in web-based health monitoring. It examines current methodologies, real-world applications, challenges, and future directions.Our findings highlights its potential to enhance early disease detection, patient sentiment analysis, healthcare delivery and public health decision-making Text mining has become a powerful tool for processing unstructured textual data in healthcare. This review explores its application in web-based health monitoring, focusing on methods, case studies, and key challenges. We identify gaps in leveraging large-scale data from online platforms for predictive healthcare insights and propose future directions.