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Identifying Patient Populations
Laura K. Wiley, PhD
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.
Register for this course and see more details by visiting:
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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
Tools: Clinical Data Types
Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.
Techniques: Data Manipulations and Combinations
Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.
Techniques: Algorithm Selection and Portability
Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension
Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.
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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
Introductory patient population identification course
According to students, this introductory course on identifying patient populations requires a working knowledge of R, which is not provided within the course. Hands-on teaching is used in addition to lectures throughout the course, but learners found this course to have many difficult exams.
Incorporates hands-on teaching methods.
"The instructor does a great job of providing hands-on teaching in addition to lecture."
Learners need to know R before taking this course.
"However, this course required a lot of knowledge of R, which wasn't provided in the introductory course."
Exams in this course are difficult.
"However, this course required a lot of knowledge of R, which wasn't provided in the introductory course."
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:
Good to know
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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