This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics.
This course is part of the Informed Clinical Decision Making using Deep Learning Specialization
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About this Course
Basic background in sql/postgres queries is required along with knowledge in python programming and packages such as numpy, scipy and matplotlib.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Understand the Schema of publicly available EHR databases (MIMIC-III)
Recognise the International Classification of Diseases (ICD) use
Extract and visualise descriptive statistics from clinical databases
Understand and extract key clinical outcomes such as mortality and stay of length
Skills you will gain
- International Classification of Diseases
- mining clinical databases
- Descriptive Statistics
- Electronic Health Records
- Ethics in EHR
Basic background in sql/postgres queries is required along with knowledge in python programming and packages such as numpy, scipy and matplotlib.
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Electronic Health Records and Public Databases
MIMIC III as a relational database
International Classification of Disease System
Concepts in MIMIC-III and an example of patients inclusion flowchart
About the Informed Clinical Decision Making using Deep Learning Specialization

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