A review of medical text analysis: Theory and practice

Abstract

Medical data analysis has emerged as an important driving force for smart healthcare with applications ranging from disease analysis to triage, diagnosis, and treatment. Text data plays a crucial role in providing contexts and details that other data types cannot capture alone, making its analysis an indispensable resource in medical research. Natural language processing, a key technology for analyzing and interpreting text, is essential for extracting meaningful insights from medical text data. This systematic review explores the analysis of text data in medicine, focusing on the applications of natural language processing methods. We retrieved a total of 4,784 publications from four databases. After applying rigorous exclusion criteria, 192 relevant publications are selected for in-depth analysis. These studies are evaluated from five critical perspectives: emerging trends of medical text analysis, commonly employed methodologies, major data sources, research topics, and applications in real-world problem-solving. Our analysis provides a comprehensive overview of the current state of medical text analysis, highlighting its advantages, limitations, and future potential. Finally, we identify key challenges and outline future research directions for advancing medical text analysis.