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

Fani Deligianni

Offered By

Informed Clinical Decision Making using Deep Learning SpecializationUniversity of Glasgow

About this Course

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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. 
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.

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Shareable Certificate

Earn a Certificate upon completion

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Course 1 of 5 in the

Informed Clinical Decision Making using Deep Learning Specialization

Intermediate Level

Basic background in sql/postgres queries is required along with knowledge in python programming and packages such as numpy, scipy and matplotlib.

Approx. 21 hours to complete

English

Subtitles: English

What 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

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 5 in the

Informed Clinical Decision Making using Deep Learning Specialization

Intermediate Level

Basic background in sql/postgres queries is required along with knowledge in python programming and packages such as numpy, scipy and matplotlib.

Approx. 21 hours to complete

English

Subtitles: English

Instructor

Fani Deligianni

School of Computing Science

449 Learners

5 Courses

Offered by

University of Glasgow

The University of Glasgow has been changing the world since 1451. It is a world top 100 university (THE, QS) with one of the largest research bases in the UK.

We are a member of the prestigious Russell Group of leading UK Universities with annual research income of more than £179m. The University’s #TeamUofG community is truly international with over 8000 staff and 28,0000 students from from more than 140 countries. A 2019 Time Out survey placed Glasgow in the top ten cities in the world. Ranked between Berlin and Paris, Glasgow was voted number one for both friendliness and affordability. Right now our dedicated community of staff, students and alumni is working to address the challenges of Covid-19 and understand how we can make life safer for everyone.

Syllabus - What you will learn from this course

Week

Week 1

5 hours to complete

Electronic Health Records and Public Databases

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.

5 hours to complete

6 videos (Total 53 min), 9 readings, 1 quiz

6 videos

Welcome to Informed Clinical Decision Making Specialisation1m

Welcome to the course1m

Big Data in Healthcare20m

EHR System in the UK and USA12m

MIMIC Critical Care Dataset: The Impact7m

Data Usage Requirements9m

9 readings

Specialization and course structure10m

Meet the team10m

Standardized electronic health record data10m

Migrating to electronic health record systems1h 30m

The Belmont Report1h

Hardware requirements5m

Obtaining access to the MIMIC-III Dataset10m

Complete CITI course 10m

MIMIC III - installation instructions5m

1 practice exercise

Week 1 Summary Quiz30m

Week

Week 2

6 hours to complete

MIMIC III as a relational database

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.

6 hours to complete

5 videos (Total 65 min), 7 readings, 1 quiz

5 videos

MIMIC-III Data Linkage10m

MIMIC-III as a Relational Database12m

MIMIC-III - Descriptive Statistics16m

Mortality and Length of Stay in MIMIC10m

Vital Signs Extraction for a Single Patient15m

7 readings

MIMIC-III, a freely accessible critical care database1h

Practical Exercise: Extract heart rate data using Postgres and Python30m

Practical Exercise: Extract hospitalisation numbers using Postgres and Python30m

Practical Exercise: Extract age and gender using Postgres and Python30m

Practical Exercise: Extract mortality numbers using Postgres and Python30m

Practical Exercise: Extract length of stay numbers using Postgres and Python30m

Practical Exercise: Extract vital data from a single patient using Postgres and Python30m

1 practice exercise

Week 2 Summary quiz30m

Week

Week 3

5 hours to complete

International Classification of Disease System

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.

5 hours to complete

5 videos (Total 53 min), 6 readings, 1 quiz

5 videos

Introduction to International Classification of Disease System11m

Evolution of the ICD System12m

ICD-9 and MIMIC-III10m

From ICD-9 to ICD-1012m

Special Signs in ICD-106m

6 readings

WHO and Health Statistics40m

History of the ICD10m

Practical Exercise: Extract patients' height using Postgres and Python15m

Practical Exercise: Extract table with days in ICU using Postgres and Python15m

Practical Exercise: Extract Glasgow Coma Scale using Postgres and Python30m

Practical Exercise: Extract ICD-9 related information using Postgres and Python1h 30m

1 practice exercise

Week 3 summary quiz30m

Week

Week 4

5 hours to complete

Concepts in MIMIC-III and an example of patients inclusion flowchart

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.

5 hours to complete

3 videos (Total 42 min), 11 readings, 2 quizzes

3 videos

Concepts in MIMIC III14m

Relation of Catheterization to Mortality: An Example Study10m

Data Extraction17m

11 readings

The MIMIC Code Repository: enabling reproducibility 10m

The Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically Stable30m

Practical Exercise: Extract vital data of MIMIC-III using Postgres and Python20m

Practical Exercise: Exclude ICU readmissions from the study using Postgres and Python20m

Practical Exercise: Select patients requiring mechanical ventilation using Postgres and Python2m

Practical Exercise: Exclude patients diagnosed with sepsis from the study using Postgres and Python20m

Practical Exercise: Exclude patients requiring vasopressors from the study using Postgres and Python20m

Practical Exercise: Exclude patients with a prior IAC placement from the study using Postgres and Python20m

Practical Exercise: Exclude patients admitted to the CSRU or CCU care units from the study using Postgres and Python20m

Practical Exercise: Combine all criteria and split the cohort in groups using Postgres and Python20m

Practical Excercise: Visualise IAC groups10m

2 practice exercises

Week 4 summary quiz30m

End of course summative quiz30m

About the Informed Clinical Decision Making using Deep Learning Specialization

This specialisation is for learners with experience in programming that are interested in expanding their skills in applying deep learning in Electronic Health Records and with a focus on how to translate their models into Clinical Decision Support Systems.

The main areas that would explore are: Data mining of Clinical Databases: Ethics, MIMIC III database, International Classification of Disease System and definition of common clinical outcomes. Deep learning in Electronic Health Records: From descriptive analytics to predictive analytics Explainable deep learning models for healthcare applications: What it is and why it is needed Clinical Decision Support Systems: Generalisation, bias, ‘fairness’, clinical usefulness and privacy of artificial intelligence algorithms.

Informed Clinical Decision Making using Deep Learning

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What will I get if I subscribe to this Specialization?
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Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Data mining of Clinical Databases - CDSS 1