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.
Este curso faz parte do Programa de cursos integrados AI in Healthcare
oferecido por
Programa de cursos integrados AI in HealthcareUniversidade de StanfordInformações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 5 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
O que você vai aprender
How to apply a framework for medical data mining
Ethical use of data in healthcare decisions
How to make use of data that may be inaccurate in systematic ways
What makes a good research question and how to construct a data mining workflow answer it
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 5 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível iniciante
Aprox. 12 horas para completar
Inglês
Legendas: Inglês
oferecido por
Universidade de Stanford
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Programa - O que você aprenderá com este curso
1 hora para concluir
Asking and answering questions via clinical data mining
1 hora para concluir
12 vídeos (Total 19 mín.), 2 leituras, 3 testes
12 videos
Welcome2min
Introduction to the data mining workflow1min
Real Life Example1min
Example: Finding similar patients2min
Example: Estimating risk37s
Putting patient data on timeline51s
Revisit the data mining workflow steps1min
Types of research questions2min
Research questions suited for clinical data55s
Example: making decision to treat1min
Properties that make answering a research question useful1min
Wrap Up1min
2 leituras
Study Guide Module 15min
Citations and Additional Readings5min
3 exercícios práticos
Reflection Exercise10min
Reflection Exercise10min
Knowledge Check30min
2 horas para concluir
Data available from Healthcare systems
2 horas para concluir
16 vídeos (Total 32 mín.), 3 leituras, 4 testes
16 videos
Review of key entities and the data they collect1min
Actors with different interests2min
Common data types in Healthcare2min
Strengths and weaknesses of observational data2min
Bias and error from the healthcare system perspective2min
Bias and error of exposures and outcomes50s
How a patient's exposure might be misclassified2min
How a patient's outcome could be misclassified2min
Electronic medical record data2min
Claims data2min
Pharmacy55s
Surveillance datasets and Registries1min
Population health data sets3min
A framework to assess if a data source is useful1min
Wrap Up1min
3 leituras
Video Image Credit
Study Guide Module 25min
Citations and Additional Readings5min
4 exercícios práticos
Reflection Exercise10min
Reflection Exercise10min
Reflection Exercise15min
Knowledge Check30min
1 hora para concluir
Representing time, and timing of events, for clinical data mining
1 hora para concluir
12 vídeos (Total 20 mín.), 2 leituras, 3 testes
12 videos
Introduction37s
Time, timelines, timescales and representations of time2min
Timescale: Choosing the relevant units of time29s
What affects the timescale1min
Representation of time57s
Time series and non-time series data2min
Order of events1min
Implicit representations of time1min
Different ways to put data in bins2min
Timing of exposures and outcomes3min
Clinical processes are non-stationary2min
Wrap Up1min
2 leituras
Study Guide Module 35min
Citations and Additional Readings5min
3 exercícios práticos
Reflection Exercise10min
Reflection Exercise 215min
Knowledge Check30min
2 horas para concluir
Creating analysis ready datasets from patient timelines
2 horas para concluir
18 vídeos (Total 33 mín.), 2 leituras, 3 testes
18 videos
Defining the unit of analysis52s
Using features and the presence of features2min
How to create features from structured sources1min
Standardizing features52s
Dealing with too many features3min
The origins of missing values3min
Dealing with missing values1min
Summary recommendations for missing values1min
Constructing new features1min
Examples of engineered features2min
When to consider engineered features1min
Main points about creating analysis ready datasets53s
Structured knowledge graphs2min
So what exactly is in a knowledge graph2min
What are important knowledge graphs2min
How to choose which knowledge graph to use1min
Wrap Up45s
2 leituras
Study Guide Module 45min
Citations and Additional Readings5min
3 exercícios práticos
Reflection Exercise10min
Reflection Exercise20min
Knowledge Check30min
Avaliações
Principais avaliações do INTRODUCTION TO CLINICAL DATA
por YX4 de Nov de 2020
Very clear and well-organized course. I have learned quite a bit about the different types of clinical data, why they are important, and how to transfer them to analytical useable data sets.
por NJ19 de Nov de 2020
It would be helpful to be able to see all items that need to be completed. I took the final test and it says I've only completed 2 of 5 courses.
por KS16 de Out de 2020
Nicely Framed and Executed in a simple language so anyone can catch up earliest.
por WS26 de Nov de 2020
Great course in understanding all the considerations to clinical data.
Sobre Programa de cursos integrados AI in Healthcare
Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.
Perguntas Frequentes – FAQ
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