Deep learning in Electronic Health Records - CDSS 2
Master deep learning techniques for EHR analysis, including neural networks, data preprocessing, and advanced imputation strategies for healthcare analytics.
Master deep learning techniques for EHR analysis, including neural networks, data preprocessing, and advanced imputation strategies for healthcare analytics.
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
Train and optimize various deep learning architectures for EHR data
Implement preprocessing techniques for clinical time-series data
Master different imputation strategies for handling missing values
Develop clinical prediction models using MIMIC-III database
Apply data encoding techniques for categorical and continuous variables
Skills you'll gain
This course includes:
4.03 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course covers deep learning applications in Electronic Health Records (EHR). Students learn to implement various neural network architectures including Multi-layer Perceptron, Convolutional Neural Networks, and Recurrent Neural Networks for healthcare data analysis. The curriculum addresses challenges specific to EHR data, such as missing values and heterogeneous data types, through advanced imputation techniques and encoding strategies. Practical applications focus on clinical prediction using the MIMIC-III database.
Artificial Intelligence and Multi-Layer Perceptron
Module 1 · 6 Hours to complete
Convolutional and Recurrent Neural Networks
Module 2 · 7 Hours to complete
Preprocessing and imputation of MIMIC III data
Module 3 · 9 Hours to complete
EHR Encodings for machine learning models
Module 4 · 7 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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