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AI for Healthcare

Nanodegree Program

Be at the forefront of the revolution of AI in Healthcare, and transform patient outcomes. Enable enhanced medical decision-making powered by machine learning to build the treatments of the future.

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04Days08Hrs48Min17Sec

  • Estimated time
    4 months

    At 15 hours / week

  • Enroll by
    December 7, 2022

    Get access to the classroom immediately on enrollment

  • Prerequisites
    Intermediate Python, and Experience with Machine Learning

What you will learn

  1. AI for Healthcare

    4 months to complete

    Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.

    Prerequisite knowledge

    1. Applying AI to 2D Medical Imaging Data

      Learn the fundamental skills needed to work with 2D medical imaging data and how to use AI to derive clinically-relevant insights from data gathered via different types of 2D medical imaging such as x-ray, mammography, and digital pathology. Extract 2D images from DICOM files and apply the appropriate tools to perform exploratory data analysis on them. Build different AI models for different clinical scenarios that involve 2D images and learn how to position AI tools for regulatory approval.

    2. Applying AI to 3D Medical Imaging Data

      Learn the fundamental skills needed to work with 3D medical imaging datasets and frame insights derived from the data in a clinically relevant context. Understand how these images are acquired, stored in clinical archives, and subsequently read and analyzed. Discover how clinicians use 3D medical images in practice and where AI holds most potential in their work with these images. Design and apply machine learning algorithms to solve the challenging problems in 3D medical imaging and how to integrate the algorithms into the clinical workflow.

    3. Applying AI to EHR Data

      Learn the fundamental skills to work with EHR data and build and evaluate compliant, interpretable models. You will cover EHR data privacy and security standards, how to analyze EHR data and avoid common challenges, and cover key industry code sets. By the end of the course, you will have the skills to analyze an EHR dataset, transform it to the right level, build powerful features with TensorFlow, and model the uncertainty and bias with TensorFlow Probability and Aequitas.

    4. Applying AI to Wearable Device Data

      Learn how to build algorithms that process the data collected by wearable devices and surface insights about the wearer’s health. Cover the sensors and signal processing foundation that are critical for success in this domain, including IMU, PPG, and ECG that are common to most wearable devices, and learn how to build three algorithms from real-world sensor data.

Learn with the best.

Learn with the best.

  • Nikhil Bikhchandani

    Data Scientist at Verily Life Sciences

    Nikhil Bikhchandani spent five years working with wearable devices at Google and Verily Life Sciences. His work with wearables spans many domains including cardiovascular disease, neurodegenerative diseases, and diabetes. Before Alphabet, he earned a B.S. and M.S. in EE and CS at Carnegie Mellon.

  • Emily Lindemer

    Director of Data Science & Analytics at Wellframe

    Emily is an expert in AI for both medical imaging and translational digital healthcare. She holds a PhD from Harvard-MIT's Health Sciences & Technology division and founded her own digital health company in the opioid space. She now runs the data science division of Wellframe.

  • Mazen Zawaideh

    Radiologist

    Mazen Zawaideh is a Neuroradiology Fellow at the University of Washington, where he focuses on advanced diagnostic imaging and minimally invasive therapeutics. He also served as a Radiology Consultant for Microsoft Research for AI applications in oncologic imaging.

  • Ivan Tarapov

    Sr. Program Manager at Microsoft Research

    At Microsoft Research, Ivan works on robust auto-segmentation algorithms for MRI and CT images. He has worked with Physio-Control, Stryker, Medtronic, and Abbott, where he has helped develop external and internal cardiac defibrillators, insulin pumps, telemedicine, and medical imaging systems.

  • 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.

All our programs include:

  • Real-world projects from industry experts

    With real-world projects and immersive content built in partnership with top-tier companies, you’ll master the tech skills companies want.

  • Technical mentor support

    Our knowledgeable mentors guide your learning and are focused on answering your questions, motivating you, and keeping you on track.

  • Career services

    You’ll have access to Github portfolio review and LinkedIn profile optimization to help you advance your career and land a high-paying role.

  • Flexible learning program

    Tailor a learning plan that fits your busy life. Learn at your own pace and reach your personal goals on the schedule that works best for you.

Program offerings

  • Class content

    • Real-world projects
    • Project reviews
    • Project feedback from experienced reviewers
  • Student services

    • Technical mentor support
    • Student community
  • Career services

    • Github review
    • Linkedin profile optimization

Succeed with personalized services.

We provide services customized for your needs at every step of your learning journey to ensure your success.

Get timely feedback on your projects.

  • Personalized feedback
  • Unlimited submissions and feedback loops
  • Practical tips and industry best practices
  • Additional suggested resources to improve
  • 1,400+

    project reviewers

  • 2.7M

    projects reviewed

  • 88/100

    reviewer rating

  • 1.1 hours

    avg project review turnaround time

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AI for Healthcare

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