Natural Language Processing for Enhanced Clinical Decision Support Systems
Abstract
This paper investigates the use of natural language processing (NLP) in enhancing clinical decision support systems (CDSS). By extracting and analyzing unstructured data from clinical notes, patient histories, and medical literature, NLP algorithms can provide healthcare professionals with valuable insights and recommendations. The study highlights various applications of NLP in healthcare, including disease diagnosis, treatment recommendations, and patient risk stratification. Several case studies demonstrate the effectiveness of NLP-enhanced CDSS in improving patient outcomes and reducing diagnostic errors. Challenges such as data quality, privacy concerns, and the integration of NLP tools with existing CDSS are also discussed.
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