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MIT Professional Education

Machine Learning for Healthcare

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Next dates

Jun 24—25
2 days
Cambridge, Massachusetts, USA
USD 2500
USD 1250 per day


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


  • 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).


David Sontag joined the MIT faculty in 2017 as Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES) and as Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS). He is also a principal investigat...
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