University of Glasgow
Data mining of Clinical Databases - CDSS 1
University of Glasgow

Data mining of Clinical Databases - CDSS 1

This course is part of Informed Clinical Decision Making using Deep Learning Specialization

Taught in English

Some content may not be translated

Fani Deligianni

Instructor: Fani Deligianni

2,254 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.8

(12 reviews)

Intermediate level

Recommended experience

20 hours (approximately)
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

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.8

(12 reviews)

Intermediate level

Recommended experience

20 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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This course is part of the Informed Clinical Decision Making using Deep Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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 quiz2 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 quiz1 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 quiz1 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 quizzes1 discussion prompt

Instructor

Fani Deligianni
University of Glasgow
5 Courses3,867 learners

Offered by

Recommended if you're interested in Machine Learning

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4.8

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DA
5

Reviewed on Aug 2, 2022

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5

Reviewed on Jul 19, 2023

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