Natural Language Processing and Text Mining (Turning Unstructured Data into Structured)

Abstract

The integration of natural language processing (NLP) and text mining techniques has emerged as a key approach to harnessing the potential of unstructured clinical text data. This chapter discusses the challenges posed by clinical narratives and explores the need to transform them into structured formats for improved data accessibility and analysis. The chapter navigates through key concepts, including text pre-processing, text classification, text clustering, topic modeling, and advances in language models and transformers. It highlights the dynamic interplay between these techniques and their applications in tasks ranging from disease classification to extraction of side effects. In addition, the chapter acknowledges the importance of addressing bias and ensuring model explainability in the context of clinical prediction systems. By providing a comprehensive overview, the chapter offers insights into the synergy of NLP and text mining techniques in shaping the future of biomedical AI, ultimately leading to safer, more efficient, and more informed healthcare decisions.