Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
Introduction to Clinical Data is an online course offered by the Stanford University School of Medicine which is accredited by the Accreditation Council for Continuing Medical Education (ACCME). The programme designed for beginner-level students will discuss the clinical data broadly.
Introduction to Clinical Data Certification Syllabus will walk the learners through ethical medical data mining and the elements of bias and fairness in clinical and healthcare data in making decisions about the care for the patients. By taking the programme, the students will be able to learn to build datasets and clean the clinical doubts and queries with the help of computational procedures.
Introduction to Clinical Data Certification Course, offered by Coursera, is completely designed for the beginner-level learner with flexible deadlines. Introduction to Clinical Data Certification by Coursera, tutored by Nigam Shah, David Magnus, and Steven Bagley, is the second in the five courses of AI in Healthcare Specialization.
The Highlights
- Provided by Coursera
- Approximately 11 hours of programme
- Offered by the Stanford University
- Flexible Deadlines
- Self-Paced Learning Option
- Shareable Certificate
- Beginner Level Course
- Financial Aid Available
- 100% Online Course
Programme Offerings
- English videos with multiple subtitles
- practice quizzes
- Graded Assignments with peer feedback
- graded Quizzes with feedback
- Graded Programming Assignments
- Course Videos & Readings
- EMI payment options
- 14 day refund period.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Coursera |
Introduction to Clinical Data Certification Fee will be determined depending on the number of months the learners want to stay on the course and study. The fee structure prescribed by Coursera is tabulated below:
Description | Total Fee in INR |
Course Fee, 1 month | Rs. 6,638 |
Course Fee, 3 months | Rs. 13,276 |
Course Fee, 6 months | Rs. 19,915 |
Eligibility Criteria
Certification Qualifying Details
To get the Introduction to Clinical Data Certification, the candidates will need to duly finish the whole process of the programme including course materials, lecture videos, quizzes, assignments, etc.
What you will learn
After the completion of the Introduction to Clinical Data Training, the learners will have a clear picture of the following concepts:
- Clinical Data
- Medical Data Mining
- Application of a framework for medical data mining
- Ethical use of data in healthcare decisions
- Framing better research questions and answering them with the help of a data mining workflow.
Who it is for
Introduction to Clinical Data Classes can be pursued by anyone who wants to explore the clinical data including the professionals such as -
- Doctor
- Health Inspector
- Health Officer
- Nurse
- Medical Lab Technician
Admission Details
Step 1- At first, the students will have to register and sign up on https://www.coursera.org/ to get access to the courses offered by Coursera.
Step 2 - After activating the Coursera account, the candidate can sign in.
Step 3 - Then, the candidate can search the ‘Stanford University’ in the search column, and then, the courses offered by Stanford University will appear on the screen.
Step 4 - Then, find the course ‘Introduction to Clinical Data’ in the list and click on it.
Step 5- Then, the page of the course will appear on the screen, and then, click on the option ‘enroll’. The students can enroll in the programme either free of cost or pay the fee prescribed by Coursera.
The Syllabus
Videos
- Welcome
- Introduction to the data mining workflow
- Real Life Example
- Example: Finding similar patients
- Example: Estimating risk
- Putting patient data on timeline
- Revisit the data mining workflow steps
- Types of research questions
- Research questions suited for clinical data
- Example: making decision to treat
- Properties that make answering a research question useful
- Wrap Up
Readings
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Review of the healthcare system
- Review of key entities and the data they collect
- Actors with different interests
- Common data types in Healthcare
- Strengths and weaknesses of observational data
- Bias and error from the healthcare system perspective
- Bias and error of exposures and outcomes
- How a patient's exposure might be misclassified
- How a patient's outcome could be misclassified
- Electronic medical record data
- Claims data
- Pharmacy
- Surveillance datasets and Registries
- Population health data sets
- A framework to assess if a data source is useful
- Wrap Up
Readings
- Video Image Credit
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction
- Time, timelines, timescales and representations of time
- Timescale: Choosing the relevant units of time
- What affects the timescale
- Representation of time
- Time series and non-time series data
- Order of events
- Implicit representations of time
- Different ways to put data in bins
- Timing of exposures and outcomes
- Clinical processes are non - stationary
- Wrap Up
Readings
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise1
- Reflection Exercise 2
- Knowledge Check
Videos
- Turning clinical data into something you can analyze
- Defining the unit of analysis
- Using features and the presence of features
- How to create features from structured sources
- Standardizing features
- Dealing with too many features
- The origins of missing values
- Dealing with missing values
- Summary recommendations for missing values
- Constructing new features
- Examples of engineered features
- When to consider engineered features
- Main points about creating analysis ready
- Structured knowledge graphs
- So what exactly is in a knowledge graph
- What are important knowledge graphs
- How to choose which knowledge graph to use
- Wrap Up
Readings
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction to unstructured data
- What is clinical text
- The value of clinical text
- What makes clinical text difficult to handle
- Privacy and de-identification
- A primer on Natural Language Processing
- Practical approach to processing clinical text
- Summary - Clinical text
- Overview and goals of medical imaging
- Why are images important?
- What are images?
- A typical image management process
- Summary - Images
- Overview of biomedical signals
- Why are signals important?
- What are signals?
- What are the major issues with using signals?
- Summary - Signals
- Wrap Up
Readings
- Video Image Credit
- Video Image Credit
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction to electronic phenotyping
- Challenges in electronic phenotyping
- Specifying an electronic phenotype
- Two approaches to phenotyping
- Rule-based electronic phenotyping
- Examples of rule based electronic phenotype definitions
- Constructing a rule based phenotype definition
- Probabilistic phenotyping
- Approaches for creating a probabilistic phenotype definition
- Software for probabilistic phenotype definitions
- Wrap Up
Readings
- Video Image Credit
- Study Guide Module
- Citations and Additional Readings
Practice Exercises
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
Videos
- Introduction to Research Ethics and AI
- The Belmont Report: A Framework for Research Ethics
- Ethical Issues in Data sources for AI
- Secondary Uses of Data
- Return of Results
- AI and The Learning Health System
- Ethics Summary
Readings
- Instructor Introduction
- Study Guide Module 7
Video
- Conclusion
Readings
- Final Assessment Note
- Claim CME Credit
- Full Study Guide
Practice Exercises
- Final Assessment
Instructors
Stanford Frequently Asked Questions (FAQ's)
The programme is instructed by Nigam Shah, David Magnus, and Steven Bagley who work at Stanford University.
The course can be completed within approximately 11 hours.
Yes, students can take the programme from home as it is offered completely in the online mode.
The programme is provided by the Stanford University School of Medicine.
No, the job assistance will not be given to the learners after completion of the course.