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Laura K. Wiley, PhD
This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
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What's inside
Syllabus
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
Read more
Syllabus
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Tailored to learners interested in biomedical informatics, focusing on identifying patient populations through computation
Beginner-friendly, providing a solid foundation in computational phenotyping
Covers advanced topics like data manipulation and algorithm selection, enhancing learners' skills in developing sophisticated phenotyping algorithms
Practical, hands-on experience via a real-world project, where learners develop an algorithm to identify patients with hypertension
Leverages Google Cloud's free computational environment, providing learners with access to industry-standard tools
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Reviews summary
Identifying patient populations: computational phenotyping
According to learners, this course provides a solid introduction to computational phenotyping, a method for identifying patient populations using clinical data. Many appreciate the opportunity to work with real clinical datasets and apply techniques in a practical application module. While the core concepts and methodology are generally well-explained, some students encountered significant challenges with the technical setup and usage of the Google Cloud environment. The course can be challenging, particularly for those without a strong background in programming or data manipulation, suggesting that certain prerequisites may not be sufficiently clear. Overall, it's seen as a valuable starting point for understanding the field, despite some hurdles related to the technical aspects and assumed prior knowledge.
Some topics could benefit from more detail.
"While the introduction is good, I wish some of the data manipulation techniques were covered in greater depth."
"The lectures sometimes felt a bit brief for the complexity of the material presented."
"Could benefit from more examples or detailed explanations on algorithm selection."
"A deeper dive into optimizing algorithms would be helpful."
Provides a clear overview of computational phenotyping.
"This course gave me an excellent introduction to the field of computational phenotyping."
"The initial modules explaining different clinical data types were very informative."
"I found the explanation of how to use various data types for identification quite clear."
"A good starting point for understanding the fundamentals of this technique."
Hands-on experience using a real clinical dataset.
"The practical application section where we used the hypertension dataset was the most valuable part."
"Working with a real clinical dataset made the concepts much more concrete and applicable."
"I really appreciated the chance to apply the techniques learned to a real-world problem like identifying hypertension patients."
"The hands-on project felt very relevant to actual biomedical informatics work."
Course can be challenging, assumes prior knowledge.
"This course is much harder than expected if you don't have prior programming experience."
"I felt that certain prerequisites, like familiarity with data manipulation tools, were not clearly stated or assumed too much."
"More challenging than anticipated, requires dedication to get through the technical parts."
"Assumes a level of comfort with coding and data science environments."
Significant difficulties with the Google Cloud setup.
"Setting up the Google Cloud environment was incredibly frustrating and time-consuming."
"I spent more time troubleshooting the technical platform than on the course material itself."
"The instructions for using the Google Cloud tools were not clear enough, especially for beginners."
"Needed better support or clearer guidance on the technical setup process."
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 Identifying Patient Populations with these
activities:
Review high school math principles
Show steps
To ensure success in this course, it would be beneficial to ensure knowledge and understanding of high school level mathematics.
Browse courses on
Trigonometry
Show steps
-
Review the functions and rules of algebra
-
Practice solving equations and inequalities
-
Review trigonometry basics, including the trigonometric ratios, identities, and graphs of trigonometric functions
Join a study group or discussion forum on computational phenotyping
Show steps
Participating in a study group or discussion forum will allow you to connect with other students, share ideas, and get feedback on your work.
Show steps
-
Find a study group or discussion forum online or in your local area
-
Attend the meetings or participate in the discussions
-
Share your work and get feedback from others
Follow guided tutorials on computational phenotyping
Show steps
Guided tutorials will provide you with structured and step-by-step instructions on how to perform computational phenotyping tasks.
Show steps
-
Find guided tutorials online or in textbooks
-
Follow the instructions in the tutorial
-
Complete the exercises and activities provided in the tutorial
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on computational phenotyping
Show steps
Solving practice problems will help you develop your skills in computational phenotyping and improve your understanding of the concepts.
Show steps
-
Find practice problems online or in textbooks
-
Work through the problems step-by-step
-
Check your answers against the provided solutions
Develop a computational phenotyping algorithm
Show steps
Developing a computational phenotyping algorithm will allow you to apply the concepts you have learned in the course and create something tangible that can be used to identify patient populations.
Show steps
-
Choose a specific disease or condition to focus on
-
Gather and prepare the necessary data
-
Develop and implement the algorithm
-
Evaluate the performance of the algorithm
Contribute to open-source projects related to computational phenotyping
Show steps
Contributing to open-source projects will allow you to learn from others, stay up-to-date on the latest developments in the field, and make a valuable contribution to the community.
Show steps
-
Find open-source projects related to computational phenotyping
-
Review the project documentation
-
Identify ways to contribute to the project
-
Submit your contributions to the project
Start a project to apply computational phenotyping to a real-world problem
Show steps
Starting a project will allow you to apply the concepts you have learned in the course to a real-world problem and make a meaningful contribution to the field.
Show steps
-
Identify a real-world problem that can be addressed using computational phenotyping
-
Gather and prepare the necessary data
-
Develop and implement a computational phenotyping algorithm
-
Evaluate the performance of the algorithm
-
Write a report or presentation on your findings
Review high school math principles
Show steps
To ensure success in this course, it would be beneficial to ensure knowledge and understanding of high school level mathematics.
Browse courses on
Trigonometry
Show steps
Show steps
Show steps
- Review the functions and rules of algebra
- Practice solving equations and inequalities
- Review trigonometry basics, including the trigonometric ratios, identities, and graphs of trigonometric functions
Join a study group or discussion forum on computational phenotyping
Show steps
Participating in a study group or discussion forum will allow you to connect with other students, share ideas, and get feedback on your work.
Show steps
Show steps
Show steps
- Find a study group or discussion forum online or in your local area
- Attend the meetings or participate in the discussions
- Share your work and get feedback from others
Follow guided tutorials on computational phenotyping
Show steps
Guided tutorials will provide you with structured and step-by-step instructions on how to perform computational phenotyping tasks.
Show steps
Show steps
Show steps
- Find guided tutorials online or in textbooks
- Follow the instructions in the tutorial
- Complete the exercises and activities provided in the tutorial
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve practice problems on computational phenotyping
Show steps
Solving practice problems will help you develop your skills in computational phenotyping and improve your understanding of the concepts.
Show steps
Show steps
Show steps
- Find practice problems online or in textbooks
- Work through the problems step-by-step
- Check your answers against the provided solutions
Develop a computational phenotyping algorithm
Show steps
Developing a computational phenotyping algorithm will allow you to apply the concepts you have learned in the course and create something tangible that can be used to identify patient populations.
Show steps
Show steps
Show steps
- Choose a specific disease or condition to focus on
- Gather and prepare the necessary data
- Develop and implement the algorithm
- Evaluate the performance of the algorithm
Contribute to open-source projects related to computational phenotyping
Show steps
Contributing to open-source projects will allow you to learn from others, stay up-to-date on the latest developments in the field, and make a valuable contribution to the community.
Show steps
Show steps
Show steps
- Find open-source projects related to computational phenotyping
- Review the project documentation
- Identify ways to contribute to the project
- Submit your contributions to the project
Start a project to apply computational phenotyping to a real-world problem
Show steps
Starting a project will allow you to apply the concepts you have learned in the course to a real-world problem and make a meaningful contribution to the field.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using computational phenotyping
- Gather and prepare the necessary data
- Develop and implement a computational phenotyping algorithm
- Evaluate the performance of the algorithm
- Write a report or presentation on your findings
Career center
Learners who complete Identifying Patient Populations will develop knowledge and skills
that may be useful to these careers:
Biostatistician
Biostatistician
Biostatisticians use statistics to design and interpret studies for clinical trials and solve biomedical research problems. Depending on their specialization, a Biostatistician may work on laboratory research or in a more applied setting, helping to develop new drugs, treatments, and devices. This course may help build a foundation for a Biostatistician by teaching learners how to identify patient populations using computational phenotyping algorithms.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and computer science to extract meaningful insights from raw data. They develop new algorithms and tools for data analysis and help organizations make data-driven decisions. This course may help build a foundation for a Data Scientist by teaching them how to identify patient populations using computational phenotyping algorithms.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and develop strategies to prevent and control their spread. They study the distribution and patterns of health events and diseases in populations. This course may help build a foundation for an Epidemiologist by teaching them how to identify patient populations using computational phenotyping algorithms.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use their knowledge of healthcare and information technology to improve the quality and efficiency of healthcare delivery. They develop and implement new health information systems and help organizations make data-driven decisions. This course may help build a foundation for a Health Informatics Specialist by teaching learners how to identify patient populations using computational phenotyping algorithms.
Medical Informatics Engineer
Medical Informatics Engineer
Medical Informatics Engineers use their knowledge of engineering and medicine to develop new technologies for healthcare. They design and implement new medical devices, software, and systems. This course may help build a foundation for a Medical Informatics Engineer by teaching them how to identify patient populations using computational phenotyping algorithms.
Pharmacist
Pharmacist
Pharmacists dispense medications and provide counseling to patients on how to use them safely and effectively. They also work with doctors and other healthcare professionals to manage patient care. This course may help build a foundation for a Pharmacist by teaching learners how to identify patient populations using computational phenotyping algorithms.
Public Health Analyst
Public Health Analyst
Public Health Analysts use their knowledge of public health to develop and implement programs to improve the health of communities. They work with government agencies, non-profit organizations, and other stakeholders to address public health issues. This course may help build a foundation for a Public Health Analyst by teaching learners how to identify patient populations using computational phenotyping algorithms.
Research Scientist
Research Scientist
Research Scientists conduct original research in a variety of fields, including biomedical sciences, engineering, and computer science. They design and conduct experiments, analyze data, and publish their findings in peer-reviewed journals. This course may help build a foundation for a Research Scientist by teaching learners how to identify patient populations using computational phenotyping algorithms.
Statistician
Statistician
Statisticians use statistics to collect, analyze, interpret, and present data. They work in a variety of fields, including healthcare, finance, and marketing. This course may help build a foundation for a Statistician by teaching learners how to identify patient populations using computational phenotyping algorithms.
Biomedical Engineer
Biomedical Engineer
Biomedical Engineers use their knowledge of engineering and medicine to develop new technologies for healthcare. They design and implement new medical devices, software, and systems. This course may be useful for a Biomedical Engineer who is interested in developing new computational tools for identifying patient populations.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data analysis to help organizations make better decisions. They work with stakeholders to identify and solve business problems. This course may be useful for a Business Analyst who is interested in using computational phenotyping algorithms to identify patient populations for market research or other purposes.
Clinical Research Coordinator
Clinical Research Coordinator
Clinical Research Coordinators manage clinical trials and other research studies. They work with researchers, patients, and other healthcare professionals to ensure that studies are conducted according to protocol. This course may be useful for a Clinical Research Coordinator who is interested in using computational phenotyping algorithms to identify patient populations for clinical trials.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants provide advice to healthcare organizations on how to improve their operations and efficiency. They work with clients to identify and solve problems, and develop and implement new solutions. This course may be useful for a Healthcare Consultant who is interested in using computational phenotyping algorithms to identify patient populations for new programs or services.
Medical Writer
Medical Writer
Medical Writers create and edit written content for a variety of audiences, including patients, healthcare professionals, and the general public. They work with medical experts to ensure that the content is accurate and up-to-date. This course may be useful for a Medical Writer who is interested in writing about computational phenotyping algorithms and their applications in healthcare.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts ensure that products and services meet quality standards. They work with teams to identify and resolve problems, and develop and implement new quality control measures. This course may be useful for a Quality Assurance Analyst who is interested in using computational phenotyping algorithms to identify patient populations for quality improvement initiatives.
For more career information including salaries, visit:
OpenCourser.com/course/1j3s1y/identifying
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
Identifying Patient Populations.
Provides a comprehensive overview of machine learning in medicine, including its history, methods, and applications. It valuable resource for both researchers and clinicians who want to learn more about this rapidly growing field.
Provides a comprehensive overview of deep learning for healthcare, including its history, methods, and applications. It valuable resource for both researchers and clinicians who want to learn more about this rapidly growing field.
Provides a comprehensive overview of health informatics, including its history, methods, and applications. It valuable resource for both researchers and clinicians who want to learn more about this important topic.
Provides a comprehensive overview of statistical methods in medical research, including their application in clinical trials, observational studies, and meta-analyses. It is an excellent resource for both researchers and clinicians who want to learn more about this important topic.
Save
Provides a comprehensive overview of epidemiology, including its history, methods, and applications. It is an excellent resource for both researchers and clinicians who want to learn more about this important topic.
Provides a basic overview of medical statistics, including its history, methods, and applications. It good resource for beginners who want to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1j3s1y/identifying
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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
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This course belongs to a
collection
called
Clinical Data Science. It's comprised of six courses:
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