Machine Learning for Healthcare

With massive amounts of data flowing from EMRs, wearables, and countless other new sources, the potential for machine learning and AI to transform healthcare is perhaps more drastic and profound than any other industry. However, there are unique obstacles that exist in healthcare that can make it difficult to apply machine learning. Oftentimes, data are missing, inaccurate or stored in silos. Connecting patient records across providers and insurers is a challenge due to the lack of interoperability and reliable patient identification methods. And in some cases, such as when dealing with patients with rare conditions, data is insufficient or incomplete.  

In this course, you'll gain practical knowledge that will enable you to overcome these hurdles and apply the latest advances in healthcare AI tools and techniques to: 

  • Connect health data from disparate sources (e.g. EHRs, mobile, wearables)
  • Identify patterns and determine the most effective treatments
  • Predict and improve patient and financial outcomes
  • Model disease progression
  • Enable personalized care and precision medicine





MLH Flyer

Lead Instructor(s): 

David Sontag


Jun 15, 2020 - Jun 16, 2020

Course Length: 

2 Days

Course Fee: 





  • Open
Registration for this course will close by June 1

Participant Takeaways: 

  • Understand current ML trends and opportunities that they bring in healthcare
  • Outline practical problems that impact the application
  • See how to break down data silos between patients, providers and payers
  • Discover how to deploy ML to improve patient outcomes and/or impact the financial performance of your organization
  • Grasp what predictive analytics often does not provide

Who Should Attend: 

This course will be applicable to data scientists, software engineers, software engineering managers, and those working on health outcomes data from a range of industries including insurance, pharmaceuticals, electronic health records, and health-related start-ups.


Participants should be comfortable programming in Python, performing basic data analysis, and using the machine learning toolkit Scikit-learn. Additionally participants should be familiar with machine learning (we recommend the MIT Professional Education course Machine Learning for Big Data and Text Processing: Foundations for participants who feel they need preparation in this area).

Computer requirements:

Laptops with Python and Scikit-learn installed are required. 

Program Outline: 

Note: This outline is a draft and subject to minor changes.

Day 1

8:30 am - 10:15 am

Introduction: What makes Healthcare unique?

10:15 am - 10:30 am


10:30 am - Noon

Case Studies in Risk Stratification

Noon - 1:00 pm


1:00 pm - 3:00 pm

Lab Work

3:00 pm - 3:15 pm


3:15 pm - 5:30 pm

Time-Series Modeling

Day 2:

8:30 am - 10:15 am

Predicting the Outcome of Interventions: Causal Inference from

Observational Data

10:15 am - 10:30 am


10:30 am - 12:30 pm

Lab Work

12:30 pm - 1:30 pm


1:30 pm - 3:00 pm

Interpretability of Machine Learning Models

3:00 pm - 3:15 pm


3:15 pm - 4:30 pm

Deploying Machine Learning in Healthcare Settings

4:30 pm - 5:30 pm

Conclusion: Where to Next?

Course Schedule: 

View 2020 schedule (pdf, subject to change)

This course runs 8:30 am - 5:30 pm each day.



This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please complete the Custom Programs request form for further details.


Fundamentals: Core concepts, understandings, and tools (60%) 60
Latest Developments: Recent advances and future trends (20%) 20
Industry Applications: Linking theory and real-world (20%) 20

Delivery Methods: 

Lecture: Delivery of material in a lecture format (60%) 60
Discussion or Groupwork: Participatory learning (20%) 20
Labs: Demonstrations, experiments, simulations (20%) 20


Introductory: Appropriate for a general audience (25%) 25
Specialized: Assumes experience in practice area or field (65%) 65
Advanced: In-depth explorations at the graduate level (10%) 10