Data Mining of Clinical Databases

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Data Mining of Clinical Databases

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

Instructor: Fani Deligianni

3,760 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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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
Earn a shareable career certificate

There are 4 modules in this course

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 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 videos10 readings1 assignment2 discussion prompts

6 videosTotal 53 minutes

Welcome to Informed Clinical Decision Making Specialisation1 minute
Welcome to the course1 minute
Big Data in Healthcare21 minutes
EHR System in the UK and USA13 minutes
MIMIC Critical Care Dataset: The Impact8 minutes
Data Usage Requirements10 minutes

10 readingsTotal 215 minutes

Specialization and course structure10 minutes
Meet the team10 minutes
Stay connected with UofG Online5 minutes
Standardized electronic health record data10 minutes
Migrating to electronic health record systems90 minutes
The Belmont Report60 minutes
Hardware requirements5 minutes
Obtaining access to the MIMIC-III Dataset10 minutes
Complete CITI course 10 minutes
MIMIC III - installation instructions5 minutes

1 assignmentTotal 30 minutes

Week 1 Summary Quiz30 minutes

2 discussion promptsTotal 20 minutes

Who are you and why are you here?10 minutes
Week 1 - Your experience10 minutes

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

5 videosTotal 65 minutes

MIMIC-III Data Linkage10 minutes
MIMIC-III as a Relational Database12 minutes
MIMIC-III - Descriptive Statistics16 minutes
Mortality and Length of Stay in MIMIC11 minutes
Vital Signs Extraction for a Single Patient15 minutes

7 readingsTotal 240 minutes

MIMIC-III, a freely accessible critical care database60 minutes
Practical Exercise: Extract heart rate data using Postgres and Python30 minutes
Practical Exercise: Extract hospitalisation numbers using Postgres and Python30 minutes
Practical Exercise: Extract age and gender using Postgres and Python30 minutes
Practical Exercise: Extract mortality numbers using Postgres and Python30 minutes
Practical Exercise: Extract length of stay numbers using Postgres and Python30 minutes
Practical Exercise: Extract vital data from a single patient using Postgres and Python30 minutes

1 assignmentTotal 30 minutes

Week 2 Summary quiz30 minutes

1 discussion promptTotal 10 minutes

Week 2 - Your experience10 minutes

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

5 videosTotal 53 minutes

Introduction to International Classification of Disease System12 minutes
Evolution of the ICD System12 minutes
ICD-9 and MIMIC-III11 minutes
From ICD-9 to ICD-1012 minutes
Special Signs in ICD-106 minutes

6 readingsTotal 200 minutes

WHO and Health Statistics40 minutes
History of the ICD10 minutes
Practical Exercise: Extract patients' height using Postgres and Python15 minutes
Practical Exercise: Extract table with days in ICU using Postgres and Python15 minutes
Practical Exercise: Extract Glasgow Coma Scale using Postgres and Python30 minutes
Practical Exercise: Extract ICD-9 related information using Postgres and Python90 minutes

1 assignmentTotal 30 minutes

Week 3 summary quiz30 minutes

1 discussion promptTotal 10 minutes

Week 3 - Your experience10 minutes

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 videos12 readings2 assignments1 discussion prompt

3 videosTotal 42 minutes

Concepts in MIMIC III15 minutes
Relation of Catheterization to Mortality: An Example Study10 minutes
Data Extraction17 minutes

12 readingsTotal 197 minutes

The MIMIC Code Repository: enabling reproducibility 10 minutes
The Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically Stable30 minutes
Practical Exercise: Extract vital data of MIMIC-III using Postgres and Python20 minutes
Practical Exercise: Exclude ICU readmissions from the study using Postgres and Python20 minutes
Practical Exercise: Select patients requiring mechanical ventilation using Postgres and Python2 minutes
Practical Exercise: Exclude patients diagnosed with sepsis from the study using Postgres and Python20 minutes
Practical Exercise: Exclude patients requiring vasopressors from the study using Postgres and Python20 minutes
Practical Exercise: Exclude patients with a prior IAC placement from the study using Postgres and Python20 minutes
Practical Exercise: Exclude patients admitted to the CSRU or CCU care units from the study using Postgres and Python20 minutes
Practical Exercise: Combine all criteria and split the cohort in groups using Postgres and Python20 minutes
Practical Excercise: Visualise IAC groups10 minutes
Keeping Learning with UofG Online5 minutes

2 assignmentsTotal 60 minutes

End of course summative quiz30 minutes
Week 4 summary quiz30 minutes

1 discussion promptTotal 10 minutes

Week 4 - Your experience10 minutes

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Instructor

Fani Deligianni

University of Glasgow

5 Courses6,523 learners

Offered by

University of Glasgow

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Reviewed on Jul 20, 2023

This is a great learning curve to properly introduce me into data analysis, and machine learning in healthcare data

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