Medical Coding AI — Part 1: Clinical Data Foundations & NLP Engineering
This course builds the clinical domain knowledge and NLP engineering foundation required for AI-powered medical coding. Students learn ICD-10-CM coding standards, risk adjustment mechanics, clinical documentation interpretation, medical language processing, text preprocessing pipelines, named entity recognition, multi-label learning fundamentals, long-tail data analysis, and dataset construction with annotation quality control. No deep learning experience is required. By the end of this course, students can manually code clinical notes with guideline justification, build a production-quality MedSpaCy NER pipeline with negation and section detection, and construct a properly labeled multi-label dataset ready for model training in Part 2.
- Level: intermediate
- Duration: 8 weeks
- 10 sections
- Price: $19.99
Topics: medical coding, ICD-10-CM, ICD-9-CM, GEMs crosswalk, clinical NLP, named entity recognition, risk adjustment, HCC, multi-label classification, long-tail learning, clinical documentation, MIMIC-III, spaCy, MedSpaCy, inter-rater reliability, HIPAA
What's inside
- Medical Coding AI — Part 1: Clinical Data Foundations & NLP Engineering
- Chapter 1: Introduction to Medical Coding
- Chapter 2: Risk Adjustment and HCC Models
- Chapter 3: Clinical Documentation Analysis
- Chapter 4: Medical Language Processing
- Chapter 5: Clinical Text Preprocessing
- Chapter 6: Named Entity Recognition and Concept Extraction
- Chapter 7: Multi-Label ICD-10-CM Classification
- Chapter 8: Long-Tail ICD Code Analysis
- Chapter 9: Dataset Construction and Annotation Quality Control