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Data mining of Clinical Databases - CDSS 1
  • Level Professional
  • المدة 21 ساعات hours
  • الطبع بواسطة University of Glasgow
  • Offered by

عن

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.

الوحدات

Welcome

    • 2 Videos
    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Who are you and why are you here?

2 Videos

  • Welcome to Informed Clinical Decision Making Specialisation
  • Welcome to the course

2 Readings

  • Specialization and course structure
  • Meet the team

Big Data and Electronic Health Records

    • 4 Videos
    • 7 Readings
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4 Videos

  • Big Data in Healthcare
  • EHR System in the UK and USA
  • MIMIC Critical Care Dataset: The Impact
  • Data Usage Requirements

7 Readings

  • Standardized electronic health record data
  • Migrating to electronic health record systems
  • The Belmont Report
  • Hardware requirements
  • Obtaining access to the MIMIC-III Dataset
  • Complete CITI course
  • MIMIC III - installation instructions

End of Week 1

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Week 1 Summary Quiz

1 Discussions

  • Week 1 - Your experience

MIMIC III and Descriptive Analytics

    • 3 Videos
    • 4 Readings
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3 Videos

  • MIMIC-III Data Linkage
  • MIMIC-III as a Relational Database
  • MIMIC-III - Descriptive Statistics

4 Readings

  • MIMIC-III, a freely accessible critical care database
  • Practical Exercise: Extract heart rate data using Postgres and Python
  • Practical Exercise: Extract hospitalisation numbers using Postgres and Python
  • Practical Exercise: Extract age and gender using Postgres and Python

MIMIC III and Clinical Outcomes

    • 1 Videos
    • 2 Readings
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1 Videos

  • Mortality and Length of Stay in MIMIC

2 Readings

  • Practical Exercise: Extract mortality numbers using Postgres and Python
  • Practical Exercise: Extract length of stay numbers using Postgres and Python

MIMIC III: Exploring Patient Data

    • 1 Videos
    • 1 Readings
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1 Videos

  • Vital Signs Extraction for a Single Patient

1 Readings

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

End of week 2

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Week 2 Summary quiz

1 Discussions

  • Week 2 - Your experience

The World Health Organisation and the International Classification of Disease System

    • 2 Videos
    • 2 Readings
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2 Videos

  • Introduction to International Classification of Disease System
  • Evolution of the ICD System

2 Readings

  • WHO and Health Statistics
  • History of the ICD

MIMIC III: Practising data queries

    • 3 Readings
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3 Readings

  • Practical Exercise: Extract patients' height using Postgres and Python
  • Practical Exercise: Extract table with days in ICU using Postgres and Python
  • Practical Exercise: Extract Glasgow Coma Scale using Postgres and Python

MIMIC III and ICD codes

    • 1 Videos
    • 1 Readings
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1 Videos

  • ICD-9 and MIMIC-III

1 Readings

  • Practical Exercise: Extract ICD-9 related information using Postgres and Python

ICD-9 vs ICD-10 systems

    • 2 Videos
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2 Videos

  • From ICD-9 to ICD-10
  • Special Signs in ICD-10

End of week 3

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Week 3 summary quiz

1 Discussions

  • Week 3 - Your experience

Clinical Concepts in MIMIC III

    • 1 Videos
    • 1 Readings
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1 Videos

  • Concepts in MIMIC III

1 Readings

  • The MIMIC Code Repository: enabling reproducibility

Flowchart of patient inclusion

    • 2 Videos
    • 10 Readings
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2 Videos

  • Relation of Catheterization to Mortality: An Example Study
  • Data Extraction

10 Readings

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

End of week 4

    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Week 4 summary quiz

1 Discussions

  • Week 4 - Your experience

Data mining of Clinical Databases - Summative Quiz

    • 1 Assignment
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1 Assignment

  • End of course summative quiz

Auto Summary

Explore the "Data mining of Clinical Databases - CDSS 1" course by Coursera, focusing on leveraging the MIMIC-III EHR database for machine learning in healthcare. Gain skills in database design, querying, and visual analytics to map research questions to clinical data. Ideal for professionals in Data Science & AI, the course offers Starter and Professional subscription options over a duration of 1260 minutes.

Fani Deligianni
Instructor

Fani Deligianni