AI for Healthcare
At 15 hours / week
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Intermediate Python, and Experience with Machine LearningSee detailed requirements.
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
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
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
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
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.
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 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.
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.
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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