Building a Clinical NLP Pipeline: Notes, Entities, and Reliable Outputs

Building a clinical NLP pipeline that holds up to medical scrutiny requires careful design at every stage. The data you collect, the annotations you trust, the models you choose, and the way you handle uncertainty all decide whether the pipeline becomes a reliable contributor or a quiet source of errors. This course walks through those decisions in the order they arise. You will work through written design exercises that mirror how a clinical informatics team would plan a NLP pipeline. The emphasis is on the practical tradeoffs that matter when the cost of being wrong is unusually high. What you'll learn: - Plan data collection across notes, reports, and research papers with attention to privacy and consent - Design annotation routines that produce reliable ground truth, including inter-annotator agreement - Apply core NLP techniques including named entity recognition, relation extraction, and classification - Map extracted entities to clinical ontologies such as SNOMED CT, RxNorm, and UMLS - Quantify uncertainty so downstream systems can make informed decisions about ambiguous outputs - Plan validation routines including held-out evaluation, prospective testing, and clinician review The course progresses from data and annotation through modeling, ontology mapping, uncertainty, and validation. A capstone written exercise asks you to draft a one-page design for a clinical NLP pipeline targeted at a specific clinical or research use case. This course is designed for healthcare data scientists, medical informatics students, and NLP practitioners entering healthcare. No deep clinical background is required. The course treats the pipeline as a design problem and stays informational; it does not provide clinical guidance for specific patient situations.