• Understand how machine learning methods can be used for risk stratification, understanding disease and its progression, and specific clinical applications to mammography, pathology, and cardiology
• Understand practical subtleties of machine learning from clinical data, such as physiological time-series, clinical text, and image data
• Implement and analyze models for supervised prediction, clinical NLP, interpretability analysis, and causal inference from clinical data

Lectures:

Unit 1: Overview of Clinical Care & Data (2 weeks)

• Introduction: What Makes Healthcare Unique?
• Translating Technology Into the Clinic
• Overview of Clinical Care
• Deep Dive Into Clinical Data

Unit 2: ML for Risk Stratification & Diagnosis (3 weeks)

• Risk Stratification
• Survival Modeling
• Learning from Noisy Labels
• Detecting and Mitigating Dataset Shift

Unit 3: ML with clinical text, imaging, and physiological data (2 weeks)

• Guest Lecture on Machine Learning for Pathology
• Physiological Time-Series
• Guest Lecture on Machine Learning for Mammography
• Clinical Natural Language Processing

Unit 4: Understanding disease and its progression (2 weeks)

• Differential Diagnosis
• Precision Medicine
• Disease Progression Modeling and Subtyping

Unit 5: Human Factors (3 weeks)

• Learning to Defer
• Guest Lecture on Machine Learning for Cardiology
• Interpretability
• Fairness
• Regulation of ML / AI in the US
• Automating Clinical Workflows

Unit 6: Causal Inference & Reinforcement Learning (3 weeks)

• Causal Inference from Observational Data
• Off-Policy Reinforcement Learning

Homeworks:

• Predicting In-hospital Mortality for Intensive Care Unit patients
• Fundamentals of Learning from Changing Data
• Fundamentals of Learning from Noisy Labels
• Identifying and Using Clinical Terms Found in Clinical Notes
• Deep Learning to De-Identify Clinical Notes
• Interpreting Deep Models for Chest X-Ray Diagnosis
• Fundamentals of Learning to Defer to Human Experts
• Fundamentals of Causal Inference from Observational Data