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 faz parte do Programa de cursos integrados Clinical Data Science
Identifying Patient Populations
oferecido por
Programa de cursos integrados Clinical Data ScienceSistema de Universidades do ColoradoUniversidade do Colorado
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Informações sobre o curso
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O que você vai aprender
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 - O que você aprenderá com este curso
Semana
12 horas para concluir
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
5 vídeos (total de (Total 23 mín.) min), 9 leituras, 2 testes
5 videos
Introduction to Computational Phenotyping5min
Introduction to Manual Record Review4min
Manual Record Review: Selecting Reviewers and Records6min
Manual Record Review: Tools and Techniques5min
9 leituras
Introduction to Specialization Instructors5min
Course Policies5min
Accessing Course Data and Technology Platform15min
Introduction to Course Example15min
Introduction to Manual Record Review10min
Methods - Selecting Reviewers10min
Methods - Selecting Records for Review10min
Methods - Creating Review Instruments and Protocols10min
Methods - Assessing Review Quality10min
2 exercícios práticos
Week 1 Practice Quiz8min
Week 1 Assessment16min
Semana
23 horas para concluir
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.
5 vídeos (total de (Total 19 mín.) min), 2 leituras, 2 testes
5 videos
Computational Phenotyping: Billing Data5min
Computational Phenotyping: Laboratory Data3min
Computational Phenotyping: Clinical Observations2min
Computational Phenotyping: Medications3min
2 leituras
Testing Individual Data Types30min
Note about the Assessment2min
2 exercícios práticos
Programming Exercises Practice Quiz30min
Week 2 Assessment18min
Semana
33 horas para concluir
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.
2 vídeos (total de (Total 15 mín.) min), 2 leituras, 2 testes
2 leituras
Data Manipulations30min
Data Combinations45min
2 exercícios práticos
Programming Exercises Practice Quiz30min
Week 3 Assessment25min
Semana
41 hora para concluir
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.
1 vídeo (total de (Total 4 mín.) min), 1 leitura, 1 teste
1 vídeos
1 leituras
Assessing Algorithmic Accuracy, Complexity, and Portability25min
1 exercício prático
Week 4 Assessment20min
Semana
54 horas para concluir
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.
1 leitura, 1 teste
1 leituras
Welcome to Practical Applications!5min
4.9
2 avaliaçõesMelhores avaliações
This is a well-presented course. I highly recommend.
Instrutores
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
Sobre Sistema de Universidades do ColoradoUniversidade do 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.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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.
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
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