Class | Date | Topic | Reading Assignments | Reflection Questions | Projects/Homework timelines |
---|---|---|---|---|---|
1 |
9/7 (Thu) 6-8 PM at Uris 140 |
1.1 Introduction to health and medicine data 1.2 History of AI/ML in health 1.3 Statistical challenges in health and medicine data |
1. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients 2. A Review of Challenges and Opportunities in Machine Learning for Health |
No reflection questions | Homework #1 out by midnight today |
2 | 9/15 |
2.1 Supervised Learning in Healthcare 2.2 Preventing data leakage 2.3 Learning with noisy labels 2.4 Positive and Unlabeled Learning 2.5 Shortcut Learning |
1. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis 2. Using Anchors to Estimate Clinical State without Labeled Data 3. Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission |
Reflection questions #1 due before class. |
Homework #1 continues |
3 | 9/22 |
3.1 Medical Imaging modalities 3.2 Convolutional Neural Networks, ResNet, ViT 3.3 Common tasks in medical imaging 3.4 State-of-the-art deep neural networks in medical imaging |
1. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation 2. Data-efficient and weakly supervised computational pathology on whole-slide images |
Reflection questions #2 due before class. |
Homework #1 continues |
4 | 9/29 |
5.1 Time-series modeling in health 5.2 Factorial switching dynamic models 5.3 State-space models 5.4 Deep learning for time-series modeling (RNN, LSTM, Attention) |
1. A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment
and Forecasting in ICU with Sparse, Heterogeneous Clinical Data 2. Probabilistic detection of short events, with application to critical care monitoring |
Reflection questions #3 due before class. |
Homework #2 ongoing Project proposals due |
5 | 10/6 |
4.1 Survival Modeling Basics 4.2 Censoring 4.3 Survival Modeling using Deep Learning |
1. An Introduction to Survival Analysis Math 2. Deep Survival Analysis |
Reflection questions #4 due before class. |
Homework #1 due Homework #2 out by midnight today |
6 | 10/13 |
6.1 Causal Inference in Healthcare 6.2 Introduction to Structural Causal Models, Potential Outcomes framework 6.3 Causal view of structural biases in the data 6.4 Average Treatment Effect, Conditional Average Treatment Effect, Effect of Treatment on the Treated 6.5 Causal Machine Learning |
1. Chapters 1, 2, and 3 of What If book by Miguel Hernan and James Robins 2. Death by Round Numbers: Glass-Box Machine Learning Uncovers Biases in Medical Practice |
Reflection questions #5 due before class. |
Homework #2 ongoing |
7 | 10/20 |
7.1 Overview of Markov Decision Processes 7.2 Offline Off-policy Evaluation and Learning 7.3 Model based RL, Causal view of RL 7.4 Causal view of Off-policy RL 7.5 Applications in Healthcare |
1. A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in
Intensive Care Units 2. Evaluating Reinforcement Learning Algorithms in Observational Health Settings 3. Confounding-Robust Policy Improvement |
Reflection questions #6 due before class. |
Homework #2 due Homework #3 out by midnight today |
8 | 10/27 |
8.1 Generalization of Machine Learning 8.2 Distribution Shifts: Types of distribution shifts 8.3 Domain adaptation, transfer learning 8.4 Causal view of distribution shift 8.5 Algorithms for robustness in Supervised Learning, Reinforcement Learning 8.6 Guest Lecture: Harvineet Singh, PhD (Postdoctoral Fellow, UCSF on Responsible ML for Health) |
1.The Clinician and Dataset Shift in Artificial Intelligence 2. Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US |
Reflection questions #7 due before class. |
Homework #3 ongoing |
9 | 11/3 |
9.1 Self-supervised Learning in Health 9.2 Contrastive Learning and Meta-Learning in Health 9.3 Guest Lecture: Pranav Rajpurkar, PhD Stanford (Assistant Professor, Harvard University, DBMI) |
1. Self-supervised learning in medicine and healthcare 2. Leveraging Time Irreversibility with Order-Contrastive Pre-training |
Reflection questions #8 due before class. |
Homework #3 ongoing |
10 | 11/10 |
10.1 Foundation Models Basics 10.2 Overview of Large Language Models, Foundation Models in Healthcare (Unimodal, Multimodal) 10.3 Discussion of metrics, evaluation, future directions 10.4 Guest Lecture: Monica Agarwal, PhD MIT |
1. Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data 2. UniverSeg: Universal Medical Image Segmentation 3. Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing 4. Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events 5. Optional: Leveraging medical Twitter to build a visual–language foundation model for pathology AI |
Reflection questions #9 due before class. |
Homework #3 due |
11 | 11/17 |
11.1 Interpretability for Health and Medicine 11.2 Conceptual overview of methods, ideas 11.3 Challenges of viewing interpretability narrowly 11.4 Guest lecture: Daksh Mittal (PhD student, Columbia Business School) and Yuanzhe Ma,(PhD student IE/OR) on Uncertainty quantification for interpretability in deep learning |
1. “Why did the Model Fail?”: Attributing Model Performance Changes to
Distribution Shifts 2. The false hope of current approaches to explainable artificial intelligence in health care |
Reflection questions #10 due before class. |
Projects ongoing |
12 | 12/1 |
12.1 Ethics, Safety, and Equity of ML in Healthcare 12.2 Modeling frameworks for safe and equitable ML in healthcare 12.3 Regulation of ML/AI in Healthcare 12.4 Guest Lecture: Adarsh Subbaswamy PhD; (Staff Fellow (Regulatory Scientist) at the U.S. FDA in the Division of Imaging Diagnostics and Software Reliability at the Center for Devices and Radiological Health) |
1. Ethical Machine Learning in Healthcare 2. Ethical limitations of algorithmic fairness solutions in health care machine learning 3. Artificial Intelligence and Machine Learning in Software as a Medical Device 4. Learning-to-defer for sequential medical decision-making under uncertainty |
Reflection questions #11 due before class. |
Projects ongoing |
13, 14 | 12/8, 12/15 | Project Presentations | N/A |
No reflection questions. Classroom discussion encouraged. |
Project reports due |