Natural Language Processing for Clinical Decision Support Systems: A Review of Recent Advances in Healthcare

Abstract

Natural Language Processing (NLP) has emerged as a powerful tool in healthcare, transforming the clinical decision support systems (CDSS). This study presents a comprehensive overview of recent advancements in NLP techniques and their applications in CDSS for healthcare. Leveraging unstructured clinical text data, such as electronic health records (EHRs) and medical literature, NLP enables the extraction of valuable information to enhance clinical decision-making, improve patient outcomes, and streamline healthcare processes. The study highlights the key findings in various areas of NLP application in CDSS.NLP techniques, including Named Entity Recognition (NER) and Relation Extraction (RE), effectively extract relevant clinical information from unstructured text data, identifying and classifying entities such as diseases, symptoms, medications, and procedures. This facilitates a deeper understanding of the context and aids in clinical text mining.NLP automates clinical coding and classification, streamlining the process of assigning standardized codes and classifications to clinical documents. NLP algorithms map clinical text to appropriate diagnosis codes (e.g., ICD-10) and procedure codes (e.g., CPT), supporting billing and administrative tasks.NLP-powered CDSS offers invaluable clinical decision support by analyzing clinical text and providing relevant recommendations to healthcare providers. By considering patient symptoms, medical history, and contextual information, NLP algorithms suggest potential diagnoses, treatment plans, and medication recommendations, aiding in improved patient care.NLP also plays a crucial role in clinical research and evidence-based medicine, enabling the extraction and synthesis of information from vast medical literature. This assists healthcare professionals in staying up-to-date with the latest research findings, clinical guidelines, and best practices. NLP techniques additionally support systematic reviews and meta-analyses by automatically extracting relevant data from studies.The study further explores the application of NLP in adverse event detection and pharmacovigilance. By analyzing narratives from various sources such as spontaneous reporting systems, social media, and electronic health records, NLP identifies and analyzes adverse drug events (ADEs) and other safety-related information. This contributes to early detection of potential safety issues and enhances pharmacovigilance efforts.By extracting and summarizing information from clinical notes, NLP algorithms reduce the time spent on manual chart review.NLP supports automated triaging and routing of patient messages and facilitates the identification of suitable candidates for clinical trials.