Introduction
Last updated
Last updated
Welcome to the Compendium Of Data science Informatics Implementation science Artificial intelligence and Computing for Health (CODIAC for Health)!
CODIAC for Health was created to support research and education activities in Rhode Island involving health data, computing, and technology. This community resource aims to provide the requisite knowledge and skills for advancing artificial intelligence in health, clinical and translational research, and learning health systems.
CODIAC for Health is a living resource that is organized in chapters covering topics, methods, and resources relevant to health informatics, implementation science, and data science. The chapters can be navigated using the Table of Contents or menu in the upper left corner.
Foundations
Overview of topics with links to references and resources for learning more.
Observational Research with EHR Data
Best practices, pipelines, and national resources. More to come!
Computing Skills
Unix, Julia, Python, R, and Other Basics SQL, UML, HTML, etc. - Forthcoming!
URSA Initiative in Rhode Island
Summary of local services and resources. More to come!
Health Data and Data Standards
Forthcoming!
Artificial Intelligence for Health
Forthcoming!
Exercises and Case Studies
Forthcoming!
Feedback and corrections are welcome, which can be sent directly to URSA Services and Support (ursa-help@brown.edu).
CODIAC for Health is supported in part by Institutional Development Award Number U54GM115677 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds Advance Rhode Island Clinical and Translational Research (Advance RI-CTR). Its predecessor, the Biomedical Informatics and Data Science Skills (BIDSS) Manual was funded in part by U54GM115677 and R25MH116440. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Parts of CODIAC for Health involve use of computational resources and services provided by the Center for Computation and Visualization (CCV) at Brown University.
