Machine Learning for Healthcare

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

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.

The Machine Learning for Healthcare course is designed for professionals who work directly with healthcare data to make key decisions pertaining to patient health and/or organizational performance. Relevant roles include:

• Data scientists who need to transform healthcare data into actionable insights and understand the limits of predictive analytics 
• Software engineers who want to better understand cutting-edge machine learning trends and acquire strategies for overcoming common implementation challenges 
• Medical professionals who are looking to use data for clinical applications, such as modeling disease progression and predicting patient outcomes
• Healthcare insurance professionals who want to leverage advances in machine learning to help identify and manage high-risk patients and reduce the likelihood of high-cost care
• Electronic health record (EHR) professionals who need to drive interoperability by connecting health data from disparate sources (e.g., EHRs, mobile, wearables)

Requirements

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).