Nanodegree Program–320
AI for Healthcare
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.
Friendly Websites
Get started with
AI for Healthcare
Learn
Average Time
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?
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.
Exercise files?
unable to download the torrent. Can you please upload again
Guys please upload udacity product manager nanodegree
https://www.udacity.com/course/product-manager-nanodegree–nd036
Hi there, am begging for adding AI for training and data scientist (Update) pls.
Thank you Thank you Thank you. You guys are the best.
If there is any way we can donate to you guys!!!!!
THALAIVA, you are great…