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There are 8 modules in this course
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.
What's included
12 videos2 readings3 assignments
Show info about module content
12 videos•Total 19 minutes
Welcome•3 minutes
Introduction to the data mining workflow•2 minutes
Real Life Example•2 minutes
Example: Finding similar patients•2 minutes
Example: Estimating risk•1 minute
Putting patient data on timeline•1 minute
Revisit the data mining workflow steps•2 minutes
Types of research questions•3 minutes
Research questions suited for clinical data•1 minute
Example: making decision to treat•1 minute
Properties that make answering a research question useful•1 minute
Wrap Up•1 minute
2 readings•Total 10 minutes
Study Guide Module 1•5 minutes
Citations and Additional Readings•5 minutes
3 assignments•Total 50 minutes
Reflection Exercise•10 minutes
Reflection Exercise•10 minutes
Knowledge Check•30 minutes
Data available from Healthcare systems
Module 2•2 hours to complete
Module details
What's included
16 videos3 readings4 assignments1 plugin
Show info about module content
16 videos•Total 32 minutes
Review of the healthcare system•1 minute
Review of key entities and the data they collect•2 minutes
Actors with different interests•2 minutes
Common data types in Healthcare•3 minutes
Strengths and weaknesses of observational data•3 minutes
Bias and error from the healthcare system perspective•2 minutes
Bias and error of exposures and outcomes•1 minute
How a patient's exposure might be misclassified•2 minutes
How a patient's outcome could be misclassified•3 minutes
Electronic medical record data•2 minutes
Claims data•3 minutes
Pharmacy•1 minute
Surveillance datasets and Registries•2 minutes
Population health data sets•4 minutes
A framework to assess if a data source is useful•2 minutes
Wrap Up•1 minute
3 readings•Total 10 minutes
Video Image Credit•0 minutes
Study Guide Module 2•5 minutes
Citations and Additional Readings•5 minutes
4 assignments•Total 65 minutes
Reflection Exercise•10 minutes
Reflection Exercise•10 minutes
Reflection Exercise•15 minutes
Knowledge Check•30 minutes
1 plugin•Total 15 minutes
Reflection Exercise•15 minutes
Representing time, and timing of events, for clinical data mining
Module 3•1 hour to complete
Module details
What's included
12 videos2 readings3 assignments
Show info about module content
12 videos•Total 20 minutes
Introduction•1 minute
Time, timelines, timescales and representations of time•2 minutes
Timescale: Choosing the relevant units of time•0 minutes
What affects the timescale•1 minute
Representation of time•1 minute
Time series and non-time series data•2 minutes
Order of events•1 minute
Implicit representations of time•1 minute
Different ways to put data in bins•2 minutes
Timing of exposures and outcomes•4 minutes
Clinical processes are non-stationary•2 minutes
Wrap Up•1 minute
2 readings•Total 10 minutes
Study Guide Module 3•5 minutes
Citations and Additional Readings•5 minutes
3 assignments•Total 55 minutes
Reflection Exercise•10 minutes
Reflection Exercise 2•15 minutes
Knowledge Check•30 minutes
Creating analysis ready datasets from patient timelines
Module 4•2 hours to complete
Module details
What's included
18 videos2 readings3 assignments
Show info about module content
18 videos•Total 33 minutes
Turning clinical data into something you can analyze•1 minute
Defining the unit of analysis•1 minute
Using features and the presence of features•3 minutes
How to create features from structured sources•1 minute
Standardizing features•1 minute
Dealing with too many features•4 minutes
The origins of missing values•3 minutes
Dealing with missing values•2 minutes
Summary recommendations for missing values•2 minutes
Constructing new features•1 minute
Examples of engineered features•2 minutes
When to consider engineered features•2 minutes
Main points about creating analysis ready datasets•1 minute
Structured knowledge graphs•2 minutes
So what exactly is in a knowledge graph•2 minutes
What are important knowledge graphs•3 minutes
How to choose which knowledge graph to use•2 minutes
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.
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Learner reviews
4.6
508 reviews
5 stars
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3 stars
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C
CP
5·
Reviewed on Jul 1, 2021
I like this course because duration that instrutors teach it isn't too lone it easy to understand. And you can gain more your skills.
E
EA
5·
Reviewed on Dec 31, 2024
Amazing! Outstanding! Gives much more insight even than courses released for engineers and data scientists.
C
CX
5·
Reviewed on Nov 4, 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.
Is this activity accredited for Continuing Medical Education (CME)?
Dates and Duration
Original Release Date: 08/10/2023
Expiration Date: 08/10/2026
Estimated Time to Complete: 11 hours
CME
Credits Offered: 11.00
Accreditation
The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The Stanford University School of Medicine designates this enduring material for a maximum of 11.00 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Disclosures
The Stanford University School of Medicine adheres to ACCME Criteria, Standards and Policies regarding industry support of continuing medical education. There are no relevant financial relationships with ACCME-defined commercial interests for anyone who was in control of the content of this activity.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.