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Data Mining of Clinical Databases · LearnSpace
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Data Mining of Clinical Databases

Курс от University of Glasgow
Средний≈ 21.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

ICD Coding (ICD-9/ICD-10)Clinical Data ManagementPredictive AnalyticsElectronic Medical RecordHealth Information ManagementData MiningDescriptive StatisticsDatabase DesignElectronic Medical Record SystemPredictive ModelingClinical ResearchRelational DatabasesData AccessMedical RecordsPatient FlowDescriptive AnalyticsHealth InformaticsApplied Machine LearningMedical CodingPrecision Medicine

Программа курса

4 модулей · 64 учебных материалов

01Electronic Health Records and Public Databases19 материалов

Welcome

Welcome to Informed Clinical Decision Making SpecialisationВидеоSpecialization and course structureЧтениеMeet the teamЧтениеWelcome to the courseВидео

Учитесь у экспертов

Fani Deligianni

Dr

Data Mining of Clinical Databases
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 21.1 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Часть программы вашего университета
Who are you and why are you here?Обсуждение
Stay connected with UofG OnlineЧтение

Big Data and Electronic Health Records

Big Data in HealthcareВидеоStandardized electronic health record dataЧтениеEHR System in the UK and USAВидеоMigrating to electronic health record systemsЧтениеMIMIC Critical Care Dataset: The ImpactВидеоData Usage RequirementsВидеоThe Belmont ReportЧтениеHardware requirementsЧтениеObtaining access to the MIMIC-III DatasetЧтениеComplete CITI course ЧтениеMIMIC III - installation instructionsЧтение

End of Week 1

Week 1 - Your experienceОбсуждениеWeek 1 Summary QuizЗадание
02MIMIC III as a relational database14 материалов

MIMIC III and Descriptive Analytics

MIMIC-III Data LinkageВидеоMIMIC-III as a Relational DatabaseВидеоMIMIC-III - Descriptive StatisticsВидеоMIMIC-III, a freely accessible critical care databaseЧтениеPractical Exercise: Extract heart rate data using Postgres and PythonЧтениеPractical Exercise: Extract hospitalisation numbers using Postgres and PythonЧтениеPractical Exercise: Extract age and gender using Postgres and PythonЧтение

MIMIC III and Clinical Outcomes

Mortality and Length of Stay in MIMICВидеоPractical Exercise: Extract mortality numbers using Postgres and PythonЧтениеPractical Exercise: Extract length of stay numbers using Postgres and PythonЧтение

MIMIC III: Exploring Patient Data

Vital Signs Extraction for a Single PatientВидеоPractical Exercise: Extract vital data from a single patient using Postgres and PythonЧтение

End of week 2

Week 2 - Your experienceОбсуждениеWeek 2 Summary quizЗадание
03International Classification of Disease System13 материалов

The World Health Organisation and the International Classification of Disease System

Introduction to International Classification of Disease SystemВидеоWHO and Health StatisticsЧтениеEvolution of the ICD SystemВидеоHistory of the ICDЧтение

MIMIC III: Practising data queries

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

MIMIC III and ICD codes

ICD-9 and MIMIC-IIIВидеоPractical Exercise: Extract ICD-9 related information using Postgres and PythonЧтение

ICD-9 vs ICD-10 systems

From ICD-9 to ICD-10ВидеоSpecial Signs in ICD-10Видео

End of week 3

Week 3 summary quizЗаданиеWeek 3 - Your experienceОбсуждение
04Concepts in MIMIC-III and an example of patients inclusion flowchart18 материалов

Clinical Concepts in MIMIC III

Concepts in MIMIC IIIВидеоThe MIMIC Code Repository: enabling reproducibility Чтение

Flowchart of patient inclusion

Relation of Catheterization to Mortality: An Example StudyВидеоThe Association Between Indwelling Arterial Catheters and Mortality in Hemodynamically StableЧтениеData ExtractionВидеоPractical Exercise: Extract vital data of MIMIC-III using Postgres and PythonЧтениеPractical Exercise: Exclude ICU readmissions from the study using Postgres and PythonЧтениеPractical Exercise: Select patients requiring mechanical ventilation using Postgres and PythonЧтениеPractical Exercise: Exclude patients diagnosed with sepsis from the study using Postgres and PythonЧтениеPractical Exercise: Exclude patients requiring vasopressors from the study using Postgres and PythonЧтениеPractical Exercise: Exclude patients with a prior IAC placement from the study using Postgres and PythonЧтениеPractical Exercise: Exclude patients admitted to the CSRU or CCU care units from the study using Postgres and PythonЧтениеPractical Exercise: Combine all criteria and split the cohort in groups using Postgres and PythonЧтениеPractical Excercise: Visualise IAC groupsЧтение

End of week 4

Week 4 summary quizЗаданиеWeek 4 - Your experienceОбсуждение

Data mining of Clinical Databases - Summative Quiz

End of course summative quizЗаданиеKeeping Learning with UofG OnlineЧтение