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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About this Course
최근 조회 4,883회
배울 내용
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.
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 2시간 필요
Introduction: Identifying Patient Populations
Learn about computational phenotyping and how to use the technique to identify patient populations.
5 videos (Total 23 min), 9 readings, 2 quizzes
5개의 동영상
Introduction to Computational Phenotyping5m
Introduction to Manual Record Review4m
Manual Record Review: Selecting Reviewers and Records6m
Manual Record Review: Tools and Techniques5m
9개의 읽기 자료
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
2개 연습문제
Week 1 Practice Quiz8m
Week 1 Assessment16m
주
2완료하는 데 3시간 필요
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 videos (Total 19 min), 2 readings, 2 quizzes
5개의 동영상
Computational Phenotyping: Billing Data5m
Computational Phenotyping: Laboratory Data3m
Computational Phenotyping: Clinical Observations2m
Computational Phenotyping: Medications3m
2개의 읽기 자료
Testing Individual Data Types30m
Note about the Assessment2m
2개 연습문제
Programming Exercises Practice Quiz30m
Week 2 Assessment18m
주
3완료하는 데 3시간 필요
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 videos (Total 15 min), 2 readings, 2 quizzes
2개의 읽기 자료
Data Manipulations30m
Data Combinations45m
2개 연습문제
Programming Exercises Practice Quiz30m
Week 3 Assessment25m
주
4완료하는 데 1시간 필요
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 video (Total 4 min), 1 reading, 1 quiz
1개의 동영상
1개의 읽기 자료
Assessing Algorithmic Accuracy, Complexity, and Portability25m
1개 연습문제
Week 4 Assessment20m
주
5완료하는 데 4시간 필요
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 reading, 1 quiz
1개의 읽기 자료
Welcome to Practical Applications!5m
4.9
2개의 리뷰최상위 리뷰
This is a well-presented course. I highly recommend.
강사
Laura K. Wiley, PhD
Assistant ProfessorDivision of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus
콜로라도 대학교 정보
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.
Clinical Data Science 전문 분야 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
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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