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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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. 20 hours to complete

English

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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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

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. 20 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Fani Deligianni

School of Computing Science

1,835 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 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.

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Syllabus - What you will learn from this course

Week1

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 Specialisation1mWelcome to the course1mBig Data in Healthcare20mEHR System in the UK and USA12mMIMIC Critical Care Dataset: The Impact7mData Usage Requirements9m

9 readings

Specialization and course structure10mMeet the team10mStandardized electronic health record data10mMigrating to electronic health record systems1h 30mThe Belmont Report1hHardware requirements5mObtaining access to the MIMIC-III Dataset10mComplete CITI course 10mMIMIC III - installation instructions5m

1 practice exercise

Week 1 Summary Quiz30m

Week2

Week 2

6 hours to complete

MIMIC III as a relational database

6 hours to complete

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

5 videos

MIMIC-III Data Linkage10mMIMIC-III as a Relational Database12mMIMIC-III - Descriptive Statistics16mMortality and Length of Stay in MIMIC10mVital Signs Extraction for a Single Patient15m

7 readings

MIMIC-III, a freely accessible critical care database1hPractical Exercise: Extract heart rate data using Postgres and Python30mPractical Exercise: Extract hospitalisation numbers using Postgres and Python30mPractical Exercise: Extract age and gender using Postgres and Python30mPractical Exercise: Extract mortality numbers using Postgres and Python30mPractical Exercise: Extract length of stay numbers using Postgres and Python30mPractical Exercise: Extract vital data from a single patient using Postgres and Python30m

1 practice exercise

Week 2 Summary quiz30m

Week3

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 System11mEvolution of the ICD System12mICD-9 and MIMIC-III10mFrom ICD-9 to ICD-1012mSpecial Signs in ICD-106m

6 readings

WHO and Health Statistics40mHistory of the ICD10mPractical Exercise: Extract patients' height using Postgres and Python15mPractical Exercise: Extract table with days in ICU using Postgres and Python15mPractical Exercise: Extract Glasgow Coma Scale using Postgres and Python30mPractical Exercise: Extract ICD-9 related information using Postgres and Python1h 30m

1 practice exercise

Week 3 summary quiz30m

Week4

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 III14mRelation of Catheterization to Mortality: An Example Study10mData Extraction17m

11 readings

The MIMIC Code Repository: enabling reproducibility 10mThe Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically Stable30mPractical Exercise: Extract vital data of MIMIC-III using Postgres and Python20mPractical Exercise: Exclude ICU readmissions from the study using Postgres and Python20mPractical Exercise: Select patients requiring mechanical ventilation using Postgres and Python2mPractical Exercise: Exclude patients diagnosed with sepsis from the study using Postgres and Python20mPractical Exercise: Exclude patients requiring vasopressors from the study using Postgres and Python20mPractical Exercise: Exclude patients with a prior IAC placement from the study using Postgres and Python20mPractical Exercise: Exclude patients admitted to the CSRU or CCU care units from the study using Postgres and Python20mPractical Exercise: Combine all criteria and split the cohort in groups using Postgres and Python20mPractical Excercise: Visualise IAC groups10m

2 practice exercises

Week 4 summary quiz30mEnd 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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