Course Summary
This course provides an introduction to clinical data and teaches the essential skills needed to understand and analyze clinical data. Students will learn how to read and interpret electronic health records, as well as how to use statistical methods to analyze clinical data.Key Learning Points
- Learn how to read and interpret electronic health records
- Understand the basics of statistical methods for clinical data analysis
- Explore different types of clinical data and their applications
Related Topics for further study
- Clinical Data Analysis
- Electronic Health Records
- Statistical Methods
- Health Informatics
- Clinical Research
Learning Outcomes
- Read and interpret electronic health records
- Apply statistical methods to analyze clinical data
- Understand different types of clinical data and their applications
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with electronic health records
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Advanced Clinical Data Analysis
- Introduction to Health Informatics
Related Books
Description
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.
Knowledge
- 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
Outline
- Asking and answering questions via clinical data mining
- 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
- Study Guide Module 1
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Data available from Healthcare systems
- 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
- Study Guide Module 2
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Representing time, and timing of events, for clinical data mining
- 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
- Study Guide Module 3
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise 2
- Knowledge Check
- Creating analysis ready datasets from patient timelines
- 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 datasets
- 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
- Study Guide Module 4
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Handling unstructured healthcare data: text, images, signals
- 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
- Study Guide Module 5
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Putting the pieces together: Electronic phenotyping
- 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
- Study Guide Module 6
- Citations and Additional Readings
- Reflection Exercise
- Reflection Exercise
- Knowledge Check
- Ethics
- 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
- Study Guide Module 7
- Course Conclusion
- Conclusion
- Final Assessment Note
- Full Study Guide
- Final Assessment
Summary of User Reviews
Introduction to Clinical Data is a highly rated course that provides an in-depth understanding of clinical data. Many users have found the course to be informative and engaging.Key Aspect Users Liked About This Course
The course content is comprehensive and easy to understand.Pros from User Reviews
- The course covers a wide range of topics related to clinical data.
- The instructors are knowledgeable and engaging.
- The course is well-structured and easy to follow.
- The assignments and quizzes are helpful in reinforcing the concepts.
- The course provides practical insights into clinical data analysis.
Cons from User Reviews
- Some users have found the course to be too basic.
- The course may not be suitable for advanced learners.
- The course lacks practical examples and case studies.
- The course may be too theoretical for some learners.
- The course may not be relevant to learners outside the healthcare industry.
Nigam Shah
4.6 Raiting
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