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.
Este curso forma parte de Programa Especializado - Clinical Data Science
Identifying Patient Populations
ofrecido por
Acerca de este Curso
Qué aprenderás
Create a computational phenotyping algorithm
Assess algorithm performance in the context of analytic goal.
Create combinations of at least three data types using boolean logic
Explain the impact of individual data type performance on computational phenotyping.
Programa - Qué aprenderás en este curso
Semana
1Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
5 videos (Total 23 minutos), 9 readings, 2 quizzes
5 videos
Introduction to Computational Phenotyping5m
Introduction to Manual Record Review4m
Manual Record Review: Selecting Reviewers and Records6m
Manual Record Review: Tools and Techniques5m
9 lecturas
Introduction to Specialization Instructors5m
Course Policies5m
Accessing Course Data and Technology Platform15m
Introduction to Course Example15m
Introduction to Manual Record Review10m
Methods - Selecting Reviewers10m
Methods - Selecting Records for Review10m
Methods - Creating Review Instruments and Protocols10m
Methods - Assessing Review Quality10m
2 ejercicios de práctica
Week 1 Practice Quiz8m
Week 1 Assessment16m
Semana
2Tools: 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.
5 videos (Total 19 minutos), 2 readings, 2 quizzes
5 videos
Computational Phenotyping: Billing Data5m
Computational Phenotyping: Laboratory Data3m
Computational Phenotyping: Clinical Observations2m
Computational Phenotyping: Medications3m
2 lecturas
Testing Individual Data Types30m
Note about the Assessment2m
2 ejercicios de práctica
Programming Exercises Practice Quiz30m
Week 2 Assessment18m
Semana
3Techniques: 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.
2 videos (Total 15 minutos), 2 readings, 2 quizzes
2 lecturas
Data Manipulations30m
Data Combinations45m
2 ejercicios de práctica
Programming Exercises Practice Quiz30m
Week 3 Assessment25m
Semana
4Techniques: Algorithm Selection and Portability
Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
1 video (Total 4 minutos), 1 reading, 1 quiz
1 video
1 lectura
Assessing Algorithmic Accuracy, Complexity, and Portability25m
1 ejercicio de práctica
Week 4 Assessment20m
Instructor
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
Acerca de Sistema Universitario de Colorado
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Acerca del programa especializado Clinical Data Science
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!
This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data.
By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.
This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
I live in an area that restricts access to Google products. Will I be able to complete the specialization?
Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.
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