Applying AI to EHR Data

Course

With the transition to electronic health records (EHR) over the last decade, the amount of EHR data has increased exponentially, providing an incredible opportunity to unlock this data with AI to benefit the healthcare system. Learn the fundamental skills of working with EHR data in order to build and evaluate compliant, interpretable machine learning models that account for bias and uncertainty using cutting-edge libraries and tools including Tensorflow Probability, Aequitas, and Shapley. Understand the implications of key data privacy and security standards in healthcare. Apply industry code sets, transform datasets at different EHR data levels, and use Tensorflow to engineer features.

Advanced

3 weeks

Real-world Projects

Completion Certificate

Last Updated January 12, 2022

Skills you'll learn:

Medical code sets • Shapley value • Feature engineering • TensorFlow

Prerequisites:

Data cleaning • Machine learning frameworks in Python • Basic supervised machine learning

Course Lessons

Lesson 1

Applying AI to EHR Data Introduction

This lesson will provide you with an Introduction to the EHR Data course outline, content, as well as introduce you to your instructor.

Lesson 2

EHR Data Security and Analysis

In this lesson, you will learn about the importance of data security and the different standards that apply to EHR, as well as analyzing EHR data.

Lesson 3

EHR Code Sets

In this lesson you will learn how to work with different EHR codes and how to map them properly to records.

Lesson 4

EHR Transformations & Feature Engineering

In this lesson, you'll gain skills in feature engineering and transformation of EHR.

Lesson 5

Building, Evaluating and Interpreting Models

In this final lesson, you'll be putting all of your skills together to build, evaluate and interpret ML models for Bias and Uncertainty.

Lesson 6 • Project

Project: Patient Selection for Diabetes Drug Testing

In this project students will use what they learn in the classroom to apply AI in healthcare for patient data.

Taught By The Best

Michael DAndrea

Principal Data Scientist at Genentech

Michael is on the Pharma Development Informatics team at Genentech (part of the Roche Group), where he works on improving clinical trials and developing safer, personalized treatments with clinical and EHR data. Previously, he was a Lead Data Scientist on the AI team at McKesson's Change Healthcare.

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Demonstrate proficiency with practical projects

Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

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Top-tier services to ensure learner success

Reviewers provide timely and constructive feedback on your project submissions, highlighting areas of improvement and offering practical tips to enhance your work.