Syllabus

Weekly Materials

Tentative and can change slightly based as the course progresses

Week 1: Introduction to NLP in Healthcare

• Introduction and getting to know each other
• Overview of the class structure (open to updates and changes)
• Paper presentations
• 2-3 papers per class
• Project overview
• Overview of NLP applications in health
• Discussion on course projects
• Topics of interest
• Teams
• Timing and final presentations
• Break 2-2:15
• 2:15-3:15 HPC tutorial by Jake Messick

Week 2: Medical Ontologies and UMLS

• Importance of ontologies in health NLP
• UMLS and its applications with paper discussions
• Project updates

Week 3: Named Entity Recognition (NER) in Medical Texts

• Key methods and tools in NER for healthcare
• Review of recent NER studies
• Project updates

Week 4: Relation Extraction in Healthcare

• Techniques and challenges in extracting relations
• Case studies and paper discussions
• Project updates

Week 5: Datasets

• Exploration of datasets specific to health research
• Clinical data vs. social media
• Project updates

Week 6: Traditional Machine Learning vs. Large Language Models in Health

• Overview of traditional methods in health
• Overview of LLM in health
• Project updates

Week 7: Predictive Modeling -session 1

• Choose from three main categories
• Symptoms discovery
• Disease detection
• Decision support systems

Week 8: Predictive Modeling -session 2

• Choose from three main categories
• Symptoms discovery
• Disease detection
• Decision support systems

Week 9: Mental Health and NLP

• Applications of NLP in mental health assessment
• Review of research and future possibilities

Week 10: Sentiment Analysis and Emotion Detection in Health

• Techniques and challenges in sentiment analysis
• Discussion of relevant papers in mental health contexts

Week 11: Ethics and bias in NLP for Health

• Privacy, bias, and fairness in health NLP
• Deep view on model biases
• Critical analysis of ethical issues through papers

Week 12: Explainable AI

• Importance and techniques of explainability in health models
• Interpretability and its impact on clinical integration

Week 13: Trends and Innovations and project presentations

• Emerging trends and future possibilities
• Paper discussions on cutting-edge research

Week 14: Project Presentations and Course Wrap-up

• Student project presentations
• Reflective discussions on course learnings