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.
But there are unique challenges that exist in healthcare that make it difficult to apply new technologies in the industry, including patient privacy issues, the lack of interoperability, and the diversity of digital health data. 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:
- Automate medical discoveries
- Predict patient outcomes
- Model disease progression
- Identify & manage high-risk patients
- Implement patient health initiatives
- Prevent high-cost care
- Registration opening soon
- 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
- Learn how to develop fair and unbiased algorithms
- Grasp what predictive analytics often does not provide
Who Should Attend:
This course is suitable for a wide-range of professionals and practitioners who are working (or are interested in working) in the healthcare industry. It is particularly geared toward those with an information technology or finance background, however executives and other leaders involved in finance, contracting, value-based care arrangements, ACOs, government relations, and others may benefit as well. A background in data science is helpful, but not required.
Participants should be familiar with machine learning and comfortable programming in Python, performing basic data analysis, and using the machine learning toolkit Scikit-learn.
Laptops with Python and Scikit-learn installed are required.
This course consists of four modules, each of which will be a mix of case studies, theory, and practical tidbits:
- Prediction: risk stratification
- Time-series modeling
- Causal inference: optimizing interventions
- Deploying ML in healthcare settings
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 investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Sontag’s research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning. Prior to joining MIT, Sontag was an Assistant Professor in Computer Science and Data Science at New York University’s Courant Institute of Mathematical Sciences from 2011 to 2016, and postdoctoral researcher at Microsoft Research New England from 2010 to 2011. He received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award.
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.