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.
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Day 1 |
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8:30 am - 10:15 am |
Introduction: What makes Healthcare unique? |
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10:15 am - 10:30 am |
Break |
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10:30 am - Noon |
Case Studies in Risk Stratification |
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Noon - 1:00 pm |
Lunch |
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1:00 pm - 3:00 pm |
Lab Work |
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3:00 pm - 3:15 pm |
Break |
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3:15 pm - 5:30 pm |
Time-Series Modeling |
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Day 2: |
|
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8:30 am - 10:15 am |
Predicting the Outcome of Interventions: Causal Inference from Observational Data |
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10:15 am - 10:30 am |
Break |
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10:30 am - 12:30 pm |
Lab Work |
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12:30 pm - 1:30 pm |
Lunch |
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1:30 pm - 3:00 pm |
Interpretability of Machine Learning Models |
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3:00 pm - 3:15 pm |
Break |
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3:15 pm - 4:30 pm |
Deploying Machine Learning in Healthcare Settings |
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4:30 pm - 5:30 pm |
Conclusion: Where to Next? |
The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.
How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.
What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.