Introduction to Clinical Data
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Introduction to Clinical Data
This course is part of AI in Healthcare Specialization
Instructors: Nigam Shah
40,759 already enrolled
Included with
511 reviews
511 reviews
What you'll learn
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
Skills you'll gain
- Responsible AI
- Data Ethics
- Health Disparities
- Electronic Medical Record
- Health Information Management
- Clinical Research Ethics
- Healthcare Ethics
- Text Mining
- Data Mining
- Unstructured Data
- Data Preprocessing
- Medical Imaging
- Data Wrangling
- Feature Engineering
- Data Collection
- Clinical Research
- Data Transformation
- Health Informatics
- Clinical Data Management
Details to know
20 assignments
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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
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
What's included
16 videos3 readings4 assignments1 plugin
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
What's included
12 videos2 readings3 assignments
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
What's included
18 videos2 readings3 assignments
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
- Wrap Up•1 minute
2 readings•Total 10 minutes
- Study Guide Module 4•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 60 minutes
- Reflection Exercise•10 minutes
- Reflection Exercise•20 minutes
- Knowledge Check•30 minutes
What's included
19 videos4 readings3 assignments
19 videos•Total 30 minutes
- Introduction to unstructured data•1 minute
- What is clinical text•1 minute
- The value of clinical text•3 minutes
- What makes clinical text difficult to handle•3 minutes
- Privacy and de-identification•3 minutes
- A primer on Natural Language Processing•1 minute
- Practical approach to processing clinical text•5 minutes
- Summary - Clinical text•1 minute
- Overview and goals of medical imaging•1 minute
- Why are images important?•0 minutes
- What are images?•3 minutes
- A typical image management process•2 minutes
- Summary - Images•1 minute
- Overview of biomedical signals•0 minutes
- Why are signals important?•1 minute
- What are signals?•1 minute
- What are the major issues with using signals?•2 minutes
- Summary - Signals•1 minute
- Wrap Up•1 minute
4 readings•Total 10 minutes
- Video Image Credit•0 minutes
- Video Image Credit•0 minutes
- Study Guide Module 5•5 minutes
- Citations and Additional Readings•5 minutes
3 assignments•Total 70 minutes
- Reflection Exercise•30 minutes
- Reflection Exercise•10 minutes
- Knowledge Check•30 minutes
What's included
11 videos3 readings3 assignments
11 videos•Total 18 minutes
- Introduction to electronic phenotyping•1 minute
- Challenges in electronic phenotyping•2 minutes
- Specifying an electronic phenotype•3 minutes
- Two approaches to phenotyping•1 minute
- Rule-based electronic phenotyping•1 minute
- Examples of rule based electronic phenotype definitions•2 minutes
- Constructing a rule based phenotype definition•1 minute
- Probabilistic phenotyping•1 minute
- Approaches for creating a probabilistic phenotype definition•3 minutes
- Software for probabilistic phenotype definitions•1 minute
- Wrap Up•2 minutes
3 readings•Total 10 minutes
- Video Image Credit•0 minutes
- Study Guide Module 6•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
What's included
7 videos2 readings
7 videos•Total 46 minutes
- Introduction to Research Ethics and AI•5 minutes
- The Belmont Report: A Framework for Research Ethics•7 minutes
- Ethical Issues in Data sources for AI•7 minutes
- Secondary Uses of Data •9 minutes
- Return of Results•6 minutes
- AI and The Learning Health System•10 minutes
- Ethics Summary•3 minutes
2 readings•Total 5 minutes
- Instructor Introduction•0 minutes
- Study Guide Module 7•5 minutes
What's included
1 video3 readings1 assignment
1 video•Total 2 minutes
- Conclusion•2 minutes
3 readings•Total 15 minutes
- Final Assessment Note•5 minutes
- Claim CME Credit•0 minutes
- Full Study Guide•10 minutes
1 assignment•Total 60 minutes
- Final Assessment•60 minutes
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Reviewed on May 31, 2022
Good introductory course, although I must admit I was expecting a little bit a more hands-on approach. Some instructors speak very fast, so I had to keep replaying the video.
Reviewed on Dec 31, 2021
Very nice and accessible introduction to clinical data and the associated ethical considerations.
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.
Frequently asked questions
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.
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.
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.
More questions
Financial aid available,