The Interplay between Natural Language Processing (NLP) and Clinical Data Mining in Healthcare: A Review - Shashank Agarwal (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Natural Language Processing (NLP) has evolved as a transformational force in the healthcare industry, which suggests innovative ways to extract, generate, and process clinical data. This review paper delves into the critical role of NLP in recasting the healthcare sector through the extraction of essential medical information from multiple sources, such as Electronic Health Records (EHRs). The objective of this review is to identify the crucial role of NLP in the healthcare industry by extracting medical information from clinical data thus augmenting patient care, disease detection, clinical decision-making, patient compliance, and even medical transcription. This review includes a detailed assessment of various NLP techniques, from rule-based techniques to statistical approaches and large language models using transfer learning. It then explores different NLP libraries and frameworks that are being widely used in a variety of fields. It also covers the types of clinical data that can be further refined and utilized through NLP, followed by several most common NLP libraries that are particularly adapted to each healthcare application. Moreover, applications and uses of NLP in healthcare systems are also discussed, paving the way for further research and its future scope in the field of health information technology. Although NLP holds a strong promising position in patient care, however, linguistic diversity, unstructured data, and semantic ambiguities are among some of the significant challenges and barriers to its implementation. Therefore, the article aims to highlight the necessity for continuous improvement and advancement in NLP techniques for ensuring the accuracy, efficiency, and reliability of data extraction, interpretation, and application within the healthcare domain.