Medical Symptom Text Classification through NLP in Healthcare

Abstract

Abstract: Medical symptom text classification through Natural Language Processing (NLP) is a rapidly evolving field that aims to leverage computational techniques to analyse and interpret vast amounts of textual data generated in healthcare settings. This paper provides a comprehensive survey of current methodologies, applications, challenges, and future directions in this domain. We begin by discussing the importance of symptom classification for improving patient outcomes, supporting clinical decisionmaking, and enhancing disease surveillance. We then review traditional machine learning approaches and advanced deep learning models, highlighting their respective strengths and limitations. Key pre-processing techniques crucial for handling medical jargon and ensuring data privacy are also examined. The paper further explores real-world applications, including clinical decision support systems, disease outbreak detection, and patient monitoring. Despite significant advancements, challenges such as data quality, model interpretability, and regulatory compliance remain. Finally, we identify emerging trends and potential future developments that could drive further innovation in NLP for healthcare. This survey aims to provide a valuable resource for researchers and practitioners seeking to understand and contribute to the field of medical symptom text classification.