Explores machine learning methods for clinical and healthcare applications. Covers concepts of algorithmic fairness, interpretability, and causality. Discusses application of time-series analysis, graphical models, deep learning and transfer learning methods to solving problems in healthcare. Considers how newly emerging machine learning techniques will shape healthcare policy and personalized medicine.
Schedule is tentative and subject to changeFeb 8, 2017
Lecture 1: What makes healthcare unique?
Lecture 2: Supervised learning for risk stratification
Lecture 3: Predicting the outcome of interventions: causal inference from observational data
Lecture 4: Ethics of machine learning to prioritize care: bias, fairness and accountability
Lecture 5: Machine learning in radiology (Guest lecture. Amir Tahmasebi and Christine Swisher, Philips Healthcare)
Lecture 6: AI for computational pathology (Guest lecture: Andrew Beck, MD, PhD, BIDMC and PathAI)
Lecture 7: Physiological and laboratory time-series
No lecture. Spring break.
Lecture 8: Preventing high-cost care (Guest lecture: Nigam Shah, MBBS, PhD, Stanford School of Medicine)
Lecture 9: Clinical text & natural language processing
Lecture 10: Interpretability of machine learning models
Lecture 11: Clustering for subtype discovery
Lecture 12: Disease progression modeling
Lecture 13: Finding optimal treatment policies
For the first problem set, please complete the steps required to get access to MIMIC data outlined here One of the steps will require you to fill out a Data Use Agreement (DUA) where you will be asked for: