Clinical NLP: Extracting Information from Health Text Data

Unstructured clinical notes and medical literature contain a wealth of critical health insights, yet extracting this information manually is nearly impossible. This course teaches you how to use modern natural language processing to automatically unlock structured data from free-form health text. By working through this written guide, you will transition from understanding basic text processing to implementing clinical information extraction pipelines. You will gain the skills to identify medical entities, map clinical concepts, and handle health data with a strong focus on privacy and modern industry standards. What you'll learn: Understand foundational medical NLP concepts and terminology before diving into clinical text analysis; Extract key clinical entities such as medications, dosages, symptoms, and diagnoses from unstructured notes; Map extracted text to standardized medical terminologies and ontologies; Apply modern transformer-based models adapted for biomedical and clinical text; Implement basic text preprocessing techniques tailored for noisy, real-world health records; Navigate privacy considerations, de-identification techniques, and ethical standards in health data processing. The course begins with core definitions and text representation basics, then guides you through entity recognition, clinical concept mapping, and advanced transformer pipelines. Designed for beginners in healthcare analytics, software developers entering the health-tech space, and data enthusiasts with no prior clinical NLP experience, this program requires no advanced prerequisites. Start reading today to transform unstructured clinical narratives into actionable, structured health insights.