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.
Data mining of Clinical Databases - CDSS 1
Data mining of Clinical Databases - CDSS 1
This course is part of Informed Clinical Decision Making using Deep Learning Specialization
Instructor: Fani Deligianni
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4.8
(13 reviews)
Intermediate level
Recommended experience
20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll 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'll gain
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Assessments
5 assignments
Taught in English
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This course is part of the Informed Clinical Decision Making using Deep Learning Specialization
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There are 4 modules in this course
This module 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. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
What's included
6 videos9 readings1 assignment2 discussion prompts
This week includes a discussion of the basic structure of MIMIC III database and practical exercises on how to extract and visualise summary statistics. We will understand the difficulty in defining clinical outcomes and we are going to examine clinical variables related to a specific patient.
What's included
5 videos7 readings1 assignment1 discussion prompt
This week discusses the history of the International Classification of Diseases (ICD) system, which has been developed collaboratively so that the medical terms and information in death certificates can be grouped together for statistical purposes. Practical examples shows how to extract ICD-9 codes from MIMIC III database and visualise them. Furthermore, we discuss differences between ICD-9, ICD-10 and ICD-11 systems.
What's included
5 videos6 readings1 assignment1 discussion prompt
This week includes an overview of clinical concepts, which are statistical tools to provide illness scores. They are developed based on expert opinion and subsequently extended based on data-driven methods. These models are the precursor of machine learning models for precision medicine. Finally, the practical exercises of this week provides the opportunity to implement a complex flowchart of patients inclusion.
What's included
3 videos11 readings2 assignments1 discussion prompt
Instructor
Offered by
Recommended if you're interested in Machine Learning
Northeastern University
Coursera Project Network
Johns Hopkins University
Johns Hopkins University
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4.8
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Showing 3 of 13
DA
5
Reviewed on Aug 2, 2022
This course is highly informative and practical-oriented. It has increased my desire in the clinical data analytics field
KD
5
Reviewed on Jul 19, 2023
This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data
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