THIS COURSE MAY BE TAKEN INDIVIDUALLY OR AS part of THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE or the Professional Certificate Program in Biotechnology & Life Sciences.

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

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.

Requirements

Laptops with Python and Scikit-learn installed are required.

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).

Program Outline

This course runs 8:30 am - 5:30 pm each day. Note: This outline is a draft and subject to minor changes.