Capstone Assignment - CDSS 5
Apply advanced clinical decision support concepts through practical assignments and real-world applications.
Apply advanced clinical decision support concepts through practical assignments and real-world applications.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Informed Clinical Decision Making using Deep Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply feature importance techniques
Implement LIME on clinical data
Use Grad-CAM for model interpretation
Analyze MIMIC database effectively
Develop practical CDSS solutions
Skills you'll gain
This course includes:
0.5 Hours PreRecorded video
3 assignments
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FullTime access
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There are 3 modules in this course
This capstone course requires students to apply knowledge from previous modules in practical scenarios. Students work with the MIMIC critical care database, implementing various interpretability techniques including permutation feature importance, LIME, and Grad-CAM on both logistic regression and LSTM models.
Permutation feature importance on the MIMIC critical care database
Module 1 · 1 Hours to complete
LIME on the MIMIC critical care database
Module 2 · 0 Hours to complete
Grad-CAM on the MIMIC critical care database
Module 3 · 0 Hours to complete
Fee Structure
Instructor
Leading Expert in Medical Image Computing and Healthcare Technology
Dr. Fani Deligianni serves as a Senior Lecturer/Associate Professor at the University of Glasgow's School of Computing Science, where she leads the Computing Technologies for Healthcare Theme. Her extensive educational background includes a PhD in Medical Image Computing from Imperial College London, two master's degrees (MSc in Advanced Computing from Imperial College London and MSc in Neuroscience from University College London), and a MEng in Electrical and Computer Engineering from Aristotle University, Greece. As a Fellow of the Higher Education Academy, she has demonstrated exceptional commitment to academic excellence and research innovation. Her research has garnered significant attention with over 50 peer-reviewed publications in prestigious venues, achieving an h-index of 22 and 2,719 citations. Her expertise in healthcare technology has attracted over £700,000 in competitive funding from organizations including EPSRC, MRC, and the Royal Society. Dr. Deligianni's research interests span medical image computing, machine learning in healthcare, human motion analysis, and brain connectivity, making her a key figure in advancing healthcare technologies through computational methods.
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