Artificial Intelligence-Based Categorization of Healthcare Text

Abstract

It is essential to collect, classify, disseminate, and manage information for healthcare systems. In modern healthcare applications, categorizing diseases is of particular importance since the decisions in healthcare are vital, and attempts to make up for them later are also very costly. For example, disease classification from patients’ symptom descriptions is crucial for determining healthcare procedures, such as online healthcare counseling and information advice. The most significant factor determining the quality of the services provided in this process is the high accuracy obtained in text classification. Text classification is the automated process of categorizing text data depending on its content. One of the main tasks of natural language processing uses fundamental data mining, text mining, and information retrieval techniques. However, with the rise of artificial intelligence, apart from traditional machine learning-based algorithms, state-of-the-art techniques such as pre-trained language models, transformers, and word embeddings are being utilized and have improved categorization accuracy in developing robust healthcare applications. This chapter presents diverse artificial intelligence-based text classification algorithms and techniques used in healthcare systems.