[Udacity] AI for Healthcare | Nanodegree Program

 

Nanodegree Program–320

AI for Healthcare

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.
  • Estimated Time
    4 months

    At 15 hours / week

Enroll by

Get access to the classroom immediately on enrollment

Prerequisites
Intermediate Python, and Experience with Machine Learning

What You Will Learn

AI for Healthcare

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

Intermediate Python, and Experience with Machine Learning

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.

Pneumonia Detection from Chest X-Rays

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.

Hippocampus Volume Quantification for Alzheimer’s Progression

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.

Patient Selection for Diabetes Drug Testing

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.

Motion Compensated Pulse Rate Estimation

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.

Get started with

AI for Healthcare

Learn
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.
Average Time
On average, successful students take null months to complete this program.
Benefits include
  • Real-world projects from industry experts
  • Technical mentor support
  • Personal career coach & career services
STAY SHARP WHILE STAYING IN
  • Financial support available worldwide to help in this challenging time
  • Spend your time at home learning new, higher-paying job skills
  • Commit to a brighter future by learning today

Program Details

PROGRAM OVERVIEW – WHY SHOULD I TAKE THIS PROGRAM?
Why should I enroll?
Artificial Intelligence has revolutionized many industries in the past decade, and healthcare is no exception. In fact, the amount of data in healthcare has grown 20x in the past 7 years, causing an expected surge in the Healthcare AI market from $2.1 to $36.1 billion by 2025 at an annual growth rate of 50.4%. AI in Healthcare is transforming the way patient care is delivered, and is impacting all aspects of the medical industry, including early detection, more accurate diagnosis, advanced treatment, health monitoring, robotics, training, research and much more.
By leveraging the power of AI, providers can deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care. In light of the worldwide COVID-19 pandemic, there has never been a better time to understand the possibilities of artificial intelligence within the healthcare industry and learn how you can make an impact to better the world’s healthcare infrastructure.
  • What jobs will this program prepare me for?
    This program will help you apply your Data Science and Machine Learning expertise in roles including Physician Data Scientist; Healthcare Data Scientist; Healthcare Data Scientist, Machine Learning; Healthcare Machine Learning Engineer, Research Scientist, Machine Learning, and more roles in the healthcare and health tech industries that necessitate knowledge of AI and machine learning techniques.
  • How do I know if this program is right for me?
    If you are interested in applying your data science and machine learning experience in the healthcare industry, then this program is right for you.
    Additional job titles and backgrounds that could be helpful include Data Scientist, Machine Learning Engineer, AI Specialist, Deep Learning Research Engineer, and AI Scientist. This program is also a good fit for Researchers, Scientists, and Engineers who want to make an impact in the medical field.
Size: 2.43GB
Course: https://www.udacity.com/course/ai-for-healthcare-nanodegree–nd320
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