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Introduction to Clinical Data · LearnSpace
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Introduction to Clinical Data

Курс от Stanford Online
Начальный≈ 11.7 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care. In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Навыки, которые вы освоите

Electronic Medical RecordFeature EngineeringData MiningData EthicsHealth DisparitiesResponsible AIClinical Data ManagementHealthcare EthicsUnstructured DataText MiningData TransformationData CollectionHealth Information ManagementMedical ImagingData WranglingClinical ResearchHealth InformaticsClinical Research EthicsData Preprocessing

Программа курса

8 модулей · 138 учебных материалов

01Asking and answering questions via clinical data mining17 материалов

Course Introduction

WelcomeВидео

The data mining workflow

Introduction to the data mining workflowВидеоReal Life ExampleВидеоExample: Finding similar patientsВидео

Учитесь у экспертов

Nigam Shah

Academic Director, AI in Healthcare Specialization; Associate Professor

Steven Bagley

Senior research engineer

David Magnus

Thomas A. Raffin Professor

Introduction to Clinical Data
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 11.7 ч

8 модулей

Язык: Английский

Субтитры: Китайский (Тайвань), Арабский, Французский, Бенгальский, Узбекский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Вьетнамский, Нидерландский, Корейский, Немецкий, Пушту, Урду, Русский, Тайский, Индонезийский, Шведский, Турецкий, Азербайджанский, Испанский, Дари, Хинди, Японский, Финский, Казахский, Малайский, Венгерский, Польский

Часть программы вашего университета
Example: Estimating riskВидео
Putting patient data on timelineВидео
Revisit the data mining workflow stepsВидео

Types of research questions

Types of research questionsВидеоReflection ExerciseЗаданиеResearch questions suited for clinical dataВидеоExample: making decision to treatВидеоProperties that make answering a research question usefulВидеоReflection ExerciseЗадание

Wrap Up

Wrap UpВидеоKnowledge CheckЗаданиеStudy Guide Module 1ЧтениеCitations and Additional ReadingsЧтение
02Data available from Healthcare systems24 материалов

The healthcare system

Review of the healthcare systemВидеоReview of key entities and the data they collectВидеоReflection ExercisePLUGINActors with different interestsВидеоVideo Image CreditЧтение

Healthcare data types

Common data types in HealthcareВидеоStrengths and weaknesses of observational dataВидеоReflection ExerciseЗадание

Sources of biases and errors

Bias and error from the healthcare system perspectiveВидеоReflection ExerciseЗаданиеBias and error of exposures and outcomesВидеоHow a patient's exposure might be misclassifiedВидеоHow a patient's outcome could be misclassifiedВидеоReflection ExerciseЗадание

Healthcare data sources

Electronic medical record dataВидеоClaims dataВидеоPharmacyВидеоSurveillance datasets and RegistriesВидеоPopulation health data setsВидеоA framework to assess if a data source is usefulВидео

Wrap Up

Wrap UpВидеоKnowledge CheckЗаданиеStudy Guide Module 2ЧтениеCitations and Additional ReadingsЧтение
03Representing time, and timing of events, for clinical data mining17 материалов

Healthcare happens over time

IntroductionВидео Time, timelines, timescales and representations of timeВидеоTimescale: Choosing the relevant units of timeВидеоReflection ExerciseЗаданиеReflection Exercise 2ЗаданиеWhat affects the timescaleВидео

Representation of time

Representation of timeВидеоTime series and non-time series dataВидеоOrder of eventsВидеоImplicit representations of timeВидеоDifferent ways to put data in binsВидеоTiming of exposures and outcomesВидео

Data change over time

Clinical processes are non-stationaryВидео

Wrap Up

Wrap UpВидеоKnowledge CheckЗаданиеStudy Guide Module 3ЧтениеCitations and Additional ReadingsЧтение
04Creating analysis ready datasets from patient timelines23 материалов

Creating features to analyze

Turning clinical data into something you can analyzeВидеоDefining the unit of analysisВидеоUsing features and the presence of featuresВидеоHow to create features from structured sourcesВидеоStandardizing featuresВидеоDealing with too many featuresВидео

Missing values

The origins of missing valuesВидеоDealing with missing valuesВидеоSummary recommendations for missing valuesВидеоReflection ExerciseЗадание

Creating new features

Constructing new featuresВидеоExamples of engineered featuresВидеоWhen to consider engineered featuresВидеоMain points about creating analysis ready datasetsВидео

Knowledge graphs

Structured knowledge graphsВидеоSo what exactly is in a knowledge graphВидеоWhat are important knowledge graphsВидеоHow to choose which knowledge graph to useВидео

Wrap Up

Wrap UpВидеоReflection ExerciseЗаданиеKnowledge CheckЗаданиеStudy Guide Module 4ЧтениеCitations and Additional ReadingsЧтение
05Handling unstructured healthcare data: text, images, signals26 материалов

Unstructured data

Introduction to unstructured dataВидео

Clinical Text

What is clinical textВидеоThe value of clinical textВидеоWhat makes clinical text difficult to handleВидеоPrivacy and de-identificationВидеоA primer on Natural Language ProcessingВидеоPractical approach to processing clinical textВидеоSummary - Clinical textВидеоReflection ExerciseЗаданиеVideo Image CreditЧтение

Images

Overview and goals of medical imagingВидеоWhy are images important?ВидеоWhat are images?ВидеоA typical image management processВидеоSummary - ImagesВидеоReflection ExerciseЗаданиеVideo Image Credit

Signals

Overview of biomedical signalsВидеоWhy are signals important?ВидеоWhat are signals?ВидеоWhat are the major issues with using signals?ВидеоSummary - SignalsВидео

Wrap Up

Wrap UpВидеоKnowledge CheckЗаданиеStudy Guide Module 5ЧтениеCitations and Additional ReadingsЧтение
06Putting the pieces together: Electronic phenotyping17 материалов

Electronic phenotyping

Introduction to electronic phenotypingВидеоChallenges in electronic phenotypingВидеоReflection ExerciseЗаданиеSpecifying an electronic phenotypeВидео

Two approaches to phenotyping

Two approaches to phenotypingВидеоRule-based electronic phenotypingВидеоExamples of rule based electronic phenotype definitionsВидеоConstructing a rule based phenotype definitionВидеоReflection ExerciseЗаданиеProbabilistic phenotypingВидеоApproaches for creating a probabilistic phenotype definitionВидеоSoftware for probabilistic phenotype definitionsВидеоVideo Image CreditЧтение

Wrap Up

Wrap UpВидеоKnowledge CheckЗаданиеStudy Guide Module 6ЧтениеCitations and Additional ReadingsЧтение
07Ethics9 материалов

Ethics - Clinical Data

Instructor IntroductionЧтениеIntroduction to Research Ethics and AIВидеоThe Belmont Report: A Framework for Research EthicsВидеоEthical Issues in Data sources for AIВидеоSecondary Uses of Data ВидеоReturn of ResultsВидеоAI and The Learning Health SystemВидеоEthics SummaryВидеоStudy Guide Module 7Чтение
08Course Conclusion5 материалов

Course Wrap Up

ConclusionВидеоFinal Assessment NoteЧтениеFinal AssessmentЗаданиеClaim CME CreditЧтениеFull Study GuideЧтение
Чтение