ABSTRACT
The domain of healthcare technology is facing many challenges in day-to-day scenarios with a major concern on the digitization process. Most conventions are being digitized for easy retrieval and analysis of healthcare data. Improving the issues and addressing the problems faced by the general population is mostly needed by medical experts and healthcare scientists. Healthcare information comes in various representations, such as images, text, categorical and continuous data, and unstructured parts. In considering the unstructured parts, the role of multimedia data plays a significant part in medical informatics. Meanwhile, data attribution plays a significant role with robustness and availability of data concerning biomedical applications. Handling structured data, such as categorical or continuous, reveals no issues or problems with analysis and interpretation. The most important part lies at the notion of handling the segments of unstructured data with tools and techniques that has been available with data analytics. Technology-based biomedical engineering created a vast difference and incorporation the following domains:
Healthcare operations
Healthcare infrastructure
Data management/record maintenance
Digital medicine
Wellness/rehabilitation
Pharmaceutical
Healthcare research
The objective of this chapter is to provide a text analytics-based e-healthcare decision support model using the incorporation of machine learning techniques and its applications. The mechanism of the text analytics-based model provides a complete way for data search, data extraction, interpretation, and evaluation. The algorithmic models and data distribution provides an active role in data generation and its constituent services. The algorithmic models corresponding to machine learning provide an efficient way of data handling and its services through text-based data retrieval with its available information retrieval models.
