Machine Learning for Healthcare (edX)

Since the 1950s with Kaiser’s first computerized records for chest X-ray reports and blood test results, and the introduction of the pacemaker, clinicians have realized the potential of algorithms to save lives. This rich history of machine learning for healthcare informs groundbreaking research today, as new advances in image processing, deep learning, and natural language processing are transforming the healthcare industry.
Using machine learning to improve patient outcomes requires that we understand the human consequences of machine learning, such as transparency, fairness, regulation, ease of deployment, and integration into clinical workflows. Throughout this course, we return to the question: how can machine learning improve healthcare for all?
The course begins with an introduction to clinical care and data, and then explores the use of machine learning for risk stratification and diagnosis, disease progression modeling, improving clinical workflows, and precision medicine. For each of these topics we dive into methodological details typically not covered in introductory machine learning courses, such as the foundations of deep learning on imaging and natural language, interpretability of ML models, algorithmic fairness, causal inference and off-policy reinforcement learning.
Guest lectures by clinicians and course programming projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.

What you'll learn

• 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

Prerequisites:

• 6.86x or equivalent machine learning course
• 6.00.1x or proficiency in Python programming
• 6.431x or equivalent probability theory course
• College-level single-variable calculus
• Vectors and matrices

Syllabus

Unit 1: Overview of Clinical Care & Data

• 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

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

Unit 3: ML with clinical text, imaging, and physiological data

• 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

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

Unit 5: Human Factors

• 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

• 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