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Laura K. Wiley, PhD
This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation, that clinical data scientists must be aware of when developing their predictive models.
Register for this course and see more details by visiting:
OpenCourser.com/course/ju8jlh/predictive
What's inside
Syllabus
Introduction: Clinical Prediction Models
Learn about the many types of clinical prediction models that exist and how they are put into practice.
Read more
Syllabus
Introduction: Clinical Prediction Models
Learn about the many types of clinical prediction models that exist and how they are put into practice.
Read more
Tools: Ensuring Model Usability
Understand how qualitative methods can be used to develop clinical prediction models that are more likely to transform clinical practice.
Techniques: Model Implementation and Sustainability
Learn about the different tools that are used to implement clinical prediction models in practice and the factors that affect implementations over time.
Techniques: Data Selection, Model Building, and Evaluation
Understand how the different types of clinical data can be used in prediction models and learn how choices made during model construction affect the utility of the model in practice.
Practical Application: Developing a Clinical Prediction Model
Put your new skills to the test - develop a clinical prediction model to asses risk of death during a stay in an Intensive Care Unit (ICU) stay.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills essential for real-world applications of predictive modeling in the clinical setting
Designed for learners who are familiar with clinical data science and have a foundational understanding of predictive modeling
Provides practical experience through a hands-on project developing a clinical prediction model to assess risk of death during an Intensive Care Unit (ICU) stay
Taught by Laura K. Wiley, PhD, a recognized expert in predictive modeling and clinical data science
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Save Predictive Modeling and Transforming Clinical Practice to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Predictive Modeling and Transforming Clinical Practice with these
activities:
Review Introduction to Predictive Modeling
Show steps
Develop a foundational understanding of predictive modeling concepts.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Establish baseline understanding of key predictive modeling concepts
-
Gain insights into the different types of predictive models and their applications
-
Explore real-world examples of how predictive models have been used to address complex problems
Participate in a Peer Learning Group
Show steps
Foster collaborative learning and reinforce course concepts.
Show steps
-
Find a group of peers with similar interests or skill levels
-
Establish regular meeting times and discussion topics
-
Share knowledge, insights, and experiences
Explore Online Tutorials on Data Preprocessing for Predictive Modeling
Show steps
Enhance understanding and practical skills in data preprocessing.
Browse courses on
Data Preprocessing
Show steps
-
Identify online tutorials that cover data preprocessing techniques
-
Follow the tutorials to learn about different methods and their applications
-
Apply the learned techniques to real-world datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a Simple Regression Model
Show steps
Solidify understanding of regression models through hands-on application.
Browse courses on
Regression Analysis
Show steps
-
Choose a dataset that is appropriate for regression analysis
-
Explore the data and identify potential relationships between variables
-
Build a simple linear regression model using appropriate statistical software
-
Evaluate the performance of the model and identify areas for improvement
Compile a Resource Guide on Predictive Modeling Tools and Techniques
Show steps
Enhance knowledge and access to predictive modeling resources.
Browse courses on
Machine Learning
Show steps
-
Identify and evaluate different predictive modeling tools and techniques
-
Summarize the key features and applications of each tool or technique
-
Organize the information into a comprehensive guide
Solve Practice Problems on Model Evaluation Metrics
Show steps
Improve understanding of model evaluation techniques through practice.
Browse courses on
Predictive Modeling
Show steps
-
Review different model evaluation metrics and their applications
-
Solve practice problems to calculate and interpret these metrics
Volunteer at a Local Hackathon Focused on Predictive Modeling
Show steps
Gain hands-on experience and connect with professionals in the field.
Browse courses on
Predictive Modeling
Show steps
-
Find a local hackathon focused on predictive modeling or machine learning
-
Attend the hackathon and participate in a team
Develop a Clinical Prediction Model
Show steps
Synthesize course concepts to create a practical clinical model.
Show steps
-
Identify a clinical problem that could be addressed with a predictive model
-
Gather and prepare relevant clinical data
-
Develop a predictive model using appropriate techniques
-
Evaluate the model's performance and refine as needed
-
Document and present the model to potential users
Review Introduction to Predictive Modeling
Show steps
Develop a foundational understanding of predictive modeling concepts.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Establish baseline understanding of key predictive modeling concepts
- Gain insights into the different types of predictive models and their applications
- Explore real-world examples of how predictive models have been used to address complex problems
Participate in a Peer Learning Group
Show steps
Foster collaborative learning and reinforce course concepts.
Show steps
Show steps
Show steps
- Find a group of peers with similar interests or skill levels
- Establish regular meeting times and discussion topics
- Share knowledge, insights, and experiences
Explore Online Tutorials on Data Preprocessing for Predictive Modeling
Show steps
Enhance understanding and practical skills in data preprocessing.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Identify online tutorials that cover data preprocessing techniques
- Follow the tutorials to learn about different methods and their applications
- Apply the learned techniques to real-world datasets
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a Simple Regression Model
Show steps
Solidify understanding of regression models through hands-on application.
Browse courses on
Regression Analysis
Show steps
Show steps
Show steps
- Choose a dataset that is appropriate for regression analysis
- Explore the data and identify potential relationships between variables
- Build a simple linear regression model using appropriate statistical software
- Evaluate the performance of the model and identify areas for improvement
Compile a Resource Guide on Predictive Modeling Tools and Techniques
Show steps
Enhance knowledge and access to predictive modeling resources.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and evaluate different predictive modeling tools and techniques
- Summarize the key features and applications of each tool or technique
- Organize the information into a comprehensive guide
Solve Practice Problems on Model Evaluation Metrics
Show steps
Improve understanding of model evaluation techniques through practice.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Review different model evaluation metrics and their applications
- Solve practice problems to calculate and interpret these metrics
Volunteer at a Local Hackathon Focused on Predictive Modeling
Show steps
Gain hands-on experience and connect with professionals in the field.
Browse courses on
Predictive Modeling
Show steps
Show steps
Show steps
- Find a local hackathon focused on predictive modeling or machine learning
- Attend the hackathon and participate in a team
Develop a Clinical Prediction Model
Show steps
Synthesize course concepts to create a practical clinical model.
Show steps
Show steps
Show steps
- Identify a clinical problem that could be addressed with a predictive model
- Gather and prepare relevant clinical data
- Develop a predictive model using appropriate techniques
- Evaluate the model's performance and refine as needed
- Document and present the model to potential users
Career center
Learners who complete Predictive Modeling and Transforming Clinical Practice will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their programming, statistical, and modeling skills to help organizations make better decisions. This course can help Data Scientists apply their skills to clinical data, allowing them to assess the risk of death during ICU stays and many other conditions. By understanding implementation techniques, sustainability factors, and tools to ensure model usability, this course can help Data Scientists develop models that have a real impact on healthcare.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models for use in a variety of industries. This course can help Machine Learning Engineers apply their skills to clinical data, allowing them to develop models that can be used to predict patient outcomes. By understanding implementation techniques and sustainability factors, this course can help Machine Learning Engineers build models that can be used to improve patient care.
Health Data Analyst
Health Data Analyst
Health Data Analysts use their skills in data analysis and visualization to help healthcare organizations make better decisions. This course can help Health Data Analysts develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Health Data Analysts can develop models that are more likely to be adopted and used by clinicians.
Clinical Data Scientist
Clinical Data Scientist
Clinical Data Scientists use their skills in data science to help improve patient care. This course can help Clinical Data Scientists develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Clinical Data Scientists can develop models that are more likely to be adopted and used by clinicians.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide advice to healthcare organizations on how to improve their operations and patient care. This course can help Healthcare Consultants develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Healthcare Consultants can make recommendations that are more likely to be adopted and implemented by healthcare organizations.
Clinical Research Associate
Clinical Research Associate
Clinical Research Associates manage clinical trials and ensure that they are conducted in accordance with ethical and regulatory guidelines. This course can help Clinical Research Associates develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Clinical Research Associates can ensure that clinical trials are designed and conducted in a way that maximizes the potential for success.
Biostatistician
Biostatistician
Biostatisticians use their skills in statistics to design and analyze clinical trials and other health-related studies. This course can help Biostatisticians develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Biostatisticians can design and analyze studies that are more likely to produce meaningful results.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes and patterns of disease in populations. This course can help Epidemiologists develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Epidemiologists can design and conduct studies that are more likely to identify the causes of disease and develop effective prevention strategies.
Medical Writer
Medical Writer
Medical Writers create written materials that communicate medical information to a variety of audiences. This course can help Medical Writers develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Medical Writers can create materials that are more likely to be accurate, clear, and persuasive.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators plan, direct, and coordinate the activities of healthcare organizations. This course can help Healthcare Administrators develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Healthcare Administrators can make informed decisions about how to allocate resources and improve patient care.
Public Health Educator
Public Health Educator
Public Health Educators develop and implement educational programs to promote health and prevent disease. This course can help Public Health Educators develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Public Health Educators can create educational programs that are more likely to be effective in promoting health and preventing disease.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks to an organization. This course can help Risk Managers develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Risk Managers can identify and assess risks to patient safety and develop strategies to mitigate those risks.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course can help Statisticians develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Statisticians can collect and analyze data in a way that maximizes the potential for meaningful results.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis and visualization to help organizations make better decisions. This course can help Data Analysts develop the skills needed to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Data Analysts can develop analyses and visualizations that are more likely to be adopted and used by healthcare organizations.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help Software Engineers develop the skills needed to build software applications that can be used to analyze clinical data and build predictive models. By understanding the challenges of clinical data implementation, Software Engineers can build applications that are more likely to be adopted and used by healthcare organizations.
For more career information including salaries, visit:
OpenCourser.com/course/ju8jlh/predictive
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Predictive Modeling and Transforming Clinical Practice.
Save
This journal is commonly cited in the literature on clinical prediction models. It provides a resource for more in-depth reading on the topic.
Provides ethical guidelines on the use of health data, including the development and implementation of prediction models.
This is valuable background reading. It provides a foundation in the principles of epidemiology, including the use of prediction models to assess population health.
Save
Provides a practical guide to epidemiology. It covers topics such as study design, data collection, and data analysis.
Provides a clinical perspective on health care economics and policy. It covers topics such as health care financing, health care delivery, and health care reform.
Save
Provides a global perspective on health care systems. It covers topics such as health care financing, health care delivery, and health care reform.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ju8jlh/predictive
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Effort
5 weeks of study, 2-4 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Colorado System
Instructor
Laura K. Wiley, PhD
Language
English
Transcripts
Español
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This course belongs to a
collection
called
Clinical Data Science. It's comprised of six courses:
- Introduction to Clinical Data Science
- Clinical Natural Language Processing
- Clinical Data Models and Data Quality Assessments
- Predictive Modeling and Transforming Clinical Practice (viewing)
- Identifying Patient Populations
- Advanced Clinical Data Science
Good to know
Develops skills essential for real-world applications of predictive modeling in the clinical setting
Designed for learners who are familiar with clinical data science and have a foundational understanding of predictive modeling
Provides practical experience through a hands-on project developing a clinical prediction model to assess risk of death during an Intensive Care Unit (ICU) stay
Taught by Laura K. Wiley, PhD, a recognized expert in predictive modeling and clinical data science
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