Machine Learning for Healthcare

Overview

What you will study

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. 

The learning objectives of the Machine Learning for Healthcare course offered by Massachusetts Institute of Technology (MIT) are:

• 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

• Explore large language models and ChatGPT

• Through lab exercises, work through applications of machine learning and causal inference on real-world health data