Data mining of Clinical Databases - CDSS 1
Master clinical database mining with MIMIC-III, learn EHR systems, and extract meaningful insights from healthcare data using SQL and Python.
Master clinical database mining with MIMIC-III, learn EHR systems, and extract meaningful insights from healthcare data using SQL and Python.
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
Understand and navigate MIMIC-III database structure
Master ICD coding systems and their clinical applications
Extract and analyze key clinical outcomes and statistics
Create comprehensive patient inclusion flowcharts
Implement SQL queries for healthcare data extraction
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
5 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces students to clinical database mining using MIMIC-III, the largest publicly available Electronic Health Record database. Students learn to navigate the database schema, understand International Classification of Diseases (ICD) coding, and extract key clinical outcomes. The curriculum covers practical skills in querying, extracting, and visualizing descriptive analytics from healthcare data, with emphasis on ethical considerations and real-world applications in clinical research.
Electronic Health Records and Public Databases
Module 1 · 5 Hours to complete
MIMIC III as a relational database
Module 2 · 5 Hours to complete
International Classification of Disease System
Module 3 · 4 Hours to complete
Concepts in MIMIC-III and patient inclusion
Module 4 · 5 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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