Text Analytics for Health: Foundations of Clinical NLP

Healthcare data is overwhelmingly unstructured, locked away in clinical notes, discharge summaries, and doctor reports. This text-only course teaches you how to leverage specialized text analytics services to extract meaningful medical insights without manual effort. You will transition from understanding basic text mining to configuring and applying health-specific natural language processing models. By practicing with real-world scenarios, you will learn to identify medical terms, map them to standard clinical vocabularies, and understand the core concepts of secure healthcare data processing. What you'll learn: • Understand the fundamentals of clinical NLP and how text analytics processes unstructured medical data • Extract key medical entities such as diagnoses, medications, dosages, and symptoms from clinical text • Map extracted terms to standard medical vocabularies like SNOMED-CT, ICD-10, and RxNorm • Detect relations between entities, such as linking a medication to its specific dosage or frequency • Determine assertion aspects, including identifying whether a condition is present or negated • Apply privacy-first design principles to handle sensitive healthcare data securely. Starting with fundamental terminology and medical NLP concepts, this course guides you through the practical mechanics of configuring text analytics workflows, analyzing sample clinical notes, and interpreting structured outputs. This course is designed for healthcare professionals, data analysts, and software developers new to clinical natural language processing, requiring no prior machine learning experience. Begin reading today to unlock the power of unstructured healthcare data.