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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3,524 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

14 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.

14 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

Relational Databases

Details to know

Shareable certificate

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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

When you enroll in this course, you'll also be enrolled in this Specialization.

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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 videos9 readings1 assignment2 discussion prompts

6 videos Total 53 minutes

Welcome to Informed Clinical Decision Making Specialisation 1 minute
Welcome to the course 1 minute
Big Data in Healthcare 21 minutes
EHR System in the UK and USA 13 minutes
MIMIC Critical Care Dataset: The Impact 8 minutes
Data Usage Requirements 10 minutes

9 readings Total 210 minutes

Specialization and course structure 10 minutes
Meet the team 10 minutes
Standardized electronic health record data 10 minutes
Migrating to electronic health record systems 90 minutes
The Belmont Report 60 minutes
Hardware requirements 5 minutes
Obtaining access to the MIMIC-III Dataset 10 minutes
Complete CITI course 10 minutes
MIMIC III - installation instructions 5 minutes

1 assignment Total 30 minutes

Week 1 Summary Quiz 30 minutes

2 discussion prompts Total 20 minutes

Who are you and why are you here? 10 minutes
Week 1 - Your experience 10 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 videos Total 65 minutes

MIMIC-III Data Linkage 10 minutes
MIMIC-III as a Relational Database 12 minutes
MIMIC-III - Descriptive Statistics 16 minutes
Mortality and Length of Stay in MIMIC 11 minutes
Vital Signs Extraction for a Single Patient 15 minutes

7 readings Total 240 minutes

MIMIC-III, a freely accessible critical care database 60 minutes
Practical Exercise: Extract heart rate data using Postgres and Python 30 minutes
Practical Exercise: Extract hospitalisation numbers using Postgres and Python 30 minutes
Practical Exercise: Extract age and gender using Postgres and Python 30 minutes
Practical Exercise: Extract mortality numbers using Postgres and Python 30 minutes
Practical Exercise: Extract length of stay numbers using Postgres and Python 30 minutes
Practical Exercise: Extract vital data from a single patient using Postgres and Python 30 minutes

1 assignment Total 30 minutes

Week 2 Summary quiz 30 minutes

1 discussion prompt Total 10 minutes

Week 2 - Your experience 10 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 videos Total 53 minutes

Introduction to International Classification of Disease System 12 minutes
Evolution of the ICD System 12 minutes
ICD-9 and MIMIC-III 11 minutes
From ICD-9 to ICD-10 12 minutes
Special Signs in ICD-10 6 minutes

6 readings Total 200 minutes

WHO and Health Statistics 40 minutes
History of the ICD 10 minutes
Practical Exercise: Extract patients' height using Postgres and Python 15 minutes
Practical Exercise: Extract table with days in ICU using Postgres and Python 15 minutes
Practical Exercise: Extract Glasgow Coma Scale using Postgres and Python 30 minutes
Practical Exercise: Extract ICD-9 related information using Postgres and Python 90 minutes

1 assignment Total 30 minutes

Week 3 summary quiz 30 minutes

1 discussion prompt Total 10 minutes

Week 3 - Your experience 10 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 videos11 readings2 assignments1 discussion prompt

3 videos Total 42 minutes

Concepts in MIMIC III 15 minutes
Relation of Catheterization to Mortality: An Example Study 10 minutes
Data Extraction 17 minutes

11 readings Total 192 minutes

The MIMIC Code Repository: enabling reproducibility 10 minutes
The Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically Stable 30 minutes
Practical Exercise: Extract vital data of MIMIC-III using Postgres and Python 20 minutes
Practical Exercise: Exclude ICU readmissions from the study using Postgres and Python 20 minutes
Practical Exercise: Select patients requiring mechanical ventilation using Postgres and Python 2 minutes
Practical Exercise: Exclude patients diagnosed with sepsis from the study using Postgres and Python 20 minutes
Practical Exercise: Exclude patients requiring vasopressors from the study using Postgres and Python 20 minutes
Practical Exercise: Exclude patients with a prior IAC placement from the study using Postgres and Python 20 minutes
Practical Exercise: Exclude patients admitted to the CSRU or CCU care units from the study using Postgres and Python 20 minutes
Practical Exercise: Combine all criteria and split the cohort in groups using Postgres and Python 20 minutes
Practical Excercise: Visualise IAC groups 10 minutes