When |
Where |
Who |
Tues 4:30-5:30 |
26-314 |
Sophie |
Wed 12:00-1:00 |
26-314 |
Ilker |
Introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows. Topics include large language models, causality, interpretability, algorithmic fairness, time-series analysis, graphical models, deep learning, transfer learning, genomics, and computational biology. Guest lectures by clinicians from the Boston area and course projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.
The schedule for classes is under revision and is still in draft form.
|
Class | Date | Lecture & Materials | Assignments |
---|---|---|---|---|
Overview of Clinical
Care & Data |
1 | Tue, Feb 4 |
Introduction: What makes healthcare unique? Reading (Due Fri, 2/7 1:00pm ET): Helpful optional readings: |
PS 0 out |
2 |
Thu, Feb 6 |
Overview of Clinical Care Reading (Due Fri, 2/7 1:00pm ET): |
||
3 |
Tue, Feb 11 |
Overview of Clinical Data Science Reading: |
PS 1 out |
|
ML with Clinical Text,
Imaging, Physiological, and Administrative Data |
4 |
Thu, Feb 13 |
ML for Risk Stratification: focus on structured EMR data Reading (Due Fri 1pm ET): |
|
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Tue, Feb 18
|
No class -- Monday schedule of classes | ||
5 | Thu, Feb 20 |
Risk Stratification and Physiological Time-Series Reading: |
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6 | Tue, Feb 25 |
LLMs 1: differential diagnosis, question answering, treatment planning Reading: |
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7 |
Thu, Feb 27 |
LLMs 2: information extraction and summarization Required Reading: Optional Reading: |
PS 2 out |
|
8 |
Tue, Mar 4 |
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9 |
Thu, Mar 6 |
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Causal Inference
|
10 |
Tue, Mar 11 |
Causal inference 1: Causal graphs, potential outcomes, covariate adjustment Reading: Optional Reading: |
|
11 |
Thu, Mar 13 |
Causal inference 2: Assumptions for causal inference, inverse propensity weighting Reading: Optional Reading: |
PS 3 out |
|
12 |
Tue, Mar 18 |
Causal inference 3: Policy learning and dynamic treatment regimes Reading: Optional Reading: |
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13 |
Thu, Mar 20 |
Dataset and temporal shift: detection and mitigation Reading: |
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Real World Deployment Challenges
|
Mar 24-28 |
Spring Break -- No classes |
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14 |
Tue, Apr 1 |
Multi-modal modeling of text and imaging data |
||
15 |
Thu, Apr 3 | Guest lecture: Faisal Mahmood (Computational Pathology) |
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16 |
Tue, Apr 8 |
Interpretability & explainability |
PS4 out |
|
17 |
Thu, Apr 10 |
Regulation of AI in healthcare |
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18 |
Tue, Apr 15 |
Human-AI collaboration in decision making |
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19 |
Thu, Apr 17 |
TBD (industry perspective) |
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20 |
Tue, Apr 22 |
Privacy: differential privacy, federated learning, synthetic data |
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21 |
Thu, Apr 24 |
TBD (industry perspective) |
|
|
22 |
Tue, Apr 29 |
Disease subtyping & progression modeling |
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23 |
Thu, May 1 |
Bias and its prevention |
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24 |
Tue, May 6 |
Guest Lecture: Jim Collins |
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25 |
Thu, May 8 |
Omics:TBD |
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26 |
Tue, May 13 |
Student Project Presentations |
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Final Exam |
Many of the lectures are associated with related papers that
should help you think about the lecture topic. For each reading
assignment, you are expected to submit a brief summary of the
three most important ideas of the paper, as short bullet points.
We are recruiting a group of doctors with interesting clinical
problems to mentor teams of students who will work on them. We
will form teams and match to problems/mentors a few weeks into the
class. We will release the project guidelines and detailed
information in Canvas.
Scenarios:
You may choose to use generative AI tools to help think through problems, but not to produce solutions
to the problem sets. In your problem set solutions, you must indicate any external resources consulted,
including Generative AI. The course staff reserves the right to ask you to explain the rationale for
any answer if it appears that it is not original to you. Pedagogically, you will learn much more from
working through any problem on your own, and this will be reflected in your final exam scores, which
will be closed-book and without access to any Generative AI.