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Fundamentals of Machine Learning for Healthcare · LearnSpace
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Fundamentals of Machine Learning for Healthcare

Курс от Stanford Online
Начальный≈ 14.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare. The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies. Co-author: Geoffrey Angus Contributing Editors: Mars Huang Jin Long Shannon Crawford Oge Marques 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

Machine Learning AlgorithmsModel TrainingModel EvaluationMachine LearningApplied Machine LearningHealth InformaticsSupervised LearningDeep LearningMachine Learning MethodsStatistical Machine LearningHealthcare EthicsMedical Science and ResearchReinforcement LearningHealth PolicyData EthicsGenerative Model ArchitecturesResponsible AIHealthcare Industry KnowledgeArtificial Neural NetworksArtificial Intelligence and Machine Learning (AI/ML)

Программа курса

8 модулей · 95 учебных материалов

01Why machine learning in healthcare?16 материалов

History of machine learning in healthcare

Getting Started: Creators of This CourseЧтениеWhy machine learning in healthcare?ВидеоHistory of AI in MedicineВидеоVideo Image CreditЧтение

Overview of course

Учитесь у экспертов

Matthew Lungren

Associate Professor

Serena Yeung

Assistant Professor

Fundamentals of Machine Learning for Healthcare
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в новой вкладке

Обучение на Coursera

≈ 14.1 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
Course OverviewВидео
Reflection ExerciseЗадание
Why Healthcare Needs Machine LearningВидео
Video Image CreditЧтение

The magic of machine learning and the different approaches

Machine Learning MagicВидеоMachine Learning, Biostatistics, ProgrammingВидеоCan Machine Learning Solve Everything?ВидеоReflection ExerciseЗаданиеKnowledge CheckЗаданиеStudy Guide Module 1ЧтениеCitations and Additional ReadingsЧтениеVideo Image CreditЧтение
02Concepts and Principles of machine learning in healthcare part 113 материалов

Machine Learning Terms, Definitions, and Jargon

Machine Learning Terms, Definitions, and Jargon Part 1ВидеоMachine Learning Terms, Definitions, and Jargon Part 2Видео

How Machines Learn

How Machines Learn Part 1ВидеоHow Machines Learn Part 2ВидеоReflection ExerciseЗадание

Supervised Machine Learning

Supervised Machine Learning Approaches: Regression and the "No Free Lunch" TheoremВидео

Traditional machine learning

Other Traditional Supervised Machine Learning ApproachesВидеоSupport Vector Machine (SVM)ВидеоUnsupervised Machine LearningВидеоReflection ExerciseЗаданиеKnowledge CheckЗаданиеStudy Guide Module 2Чтение
03Concepts and Principles of machine learning in healthcare part 216 материалов

Deep learning and neural networks

Introduction to Deep Learning and Neural NetworksВидеоDeep Learning and Neural NetworksВидео

Important concepts in deep learning

Cross Entropy LossВидеоGradient DescentВидеоRepresenting Unstructured Image and Text DataВидеоReflection ExerciseЗадание

Types of neural networks and applications

Convolutional Neural NetworksВидеоNatural Language Processing and Recurrent Neural NetworksВидеоThe Transformer Architecture for SequencesВидеоVideo Image CreditЧтение

Overview of common neural networks

Commonly Used and Advanced Neural Network ArchitecturesВидеоReflection ExerciseЗадание

Wrap Up

Advanced Computer Vision Tasks and Wrap-UpВидеоKnowledge CheckЗаданиеStudy Guide Module 3ЧтениеCitations and Additional ReadingsЧтение
04Evaluation and Metrics for machine learning in healthcare10 материалов

Critical evaluation of models and strategies for healthcare applications

Introduction to Model Performance EvaluationВидеоOverfitting and UnderfittingВидеоStrategies to Address Overfitting, Underfitting and Introduction to Regularization ВидеоStatistical Approaches to Model EvaluationВидео

Important metrics for clinical machine learning

Receiver Operator and Precision Recall Curves as Evaluation MetricsВидеоReflection Exercise 1ЗаданиеReflection Exercise 2ЗаданиеKnowledge CheckЗаданиеStudy Guide Module 4ЧтениеCitations and Additional ReadingsЧтение
05Strategies and Challenges in Machine Learning in Healthcare14 материалов

Challenges and strategies for clinical machine learning

Introduction to Common Clinical Machine Learning ChallengesВидеоUtility of Causative Model PredictionsВидеоContext in Clinical Machine LearningВидеоReflection ExerciseЗадание

Interpretability and performance of machine learning models in healthcare

Intrinsic Interpretability Видео

Medical data for machine learning

Medical Data Challenges in Machine Learning Part 1ВидеоMedical Data Challenges in Machine Learning Part 2ВидеоHow Much Data Do We Need?ВидеоRetrospective Data in Medicine and "Shelf Life" for DataВидеоMedical Data: Quality vs QuantityВидеоReflection ExerciseЗаданиеKnowledge CheckЗаданиеStudy Guide Module 5ЧтениеCitations and Additional ReadingsЧтение
06Best practices, teams, and launching your machine learning journey13 материалов

Designing and evaluating clinical machine learning applications

Clinical Utility and Output Action PairingВидеоTaking Action - Utilizing the OAP FrameworkВидеоBuilding Multidiciplinary Teams for Clinical Machine Learning ВидеоGovernance, Ethics, and Best PracticesВидеоReflection ExerciseЗадание

Human factors in clinical machine learning - from job displacement to automation bias

On Being Human in the Era of Clinical Machine LearningВидеоDeath by GPS and Other Lessons of Automation BiasВидеоReflection ExerciseЗаданиеKnowledge CheckЗаданиеStudy Guide Module 6ЧтениеCitations and Additional ReadingsЧтениеVideo Image CreditЧтение

Recommended Reading for Ethics

Recommended Reading for EthicsЧтение
07Foundation models (Optional Content)8 материалов

Foundation Models

Introduction to Foundation ModelsВидеоAdapting to TechnologyВидеоGeneral AI and Emergent BehaviorВидеоHow Foundation Models WorkВидеоHealthcare Use Cases for Text DataВидеоHealthcare Use Cases for Non-textual Unstructured DataВидеоChallenges and PitfallsВидеоConclusionВидео
08Course Conclusion5 материалов

Course Summary

Wrap Up and GoodbyesВидеоFinal Assessment NoteЧтениеFinal AssessmentЗаданиеClaim CME CreditЧтениеFull Study GuideЧтение
Citations and Additional ReadingsЧтение