Editors and other contributors to CODIAC for Health include faculty, staff, and students from:

• Brown Center for Biomedical Informatics (BCBI)

• Brown Research on Implementation and Dissemination to Guide Evidence Use (BRIDGE)

• Advance RI-CTR Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement (BIBCE) Core

• Scholarly Concentration in Biomedical Informatics (SC-BMI)

• Methods in Informatics and Data Science for Health (BIOL 1555/PHP 2561)

• Data Science Fellows (DATA 1150)

Current Contributors

• Liz Chen, PhD, FACMI (Founding Co-Editor, 2017-)

• Neil Sarkar, PhD, MLIS, ACHIP, FACMI, FAMIA (Founding Co-Editor, 2017-)

• Karen Crowley, MS, PhD (Co-Editor, 2023-)

• Jonah Bradenday, BA (Co-Editor and Illustrator, 2023-)

• Ruben Martinez, PhD (Co-Editor - Implementation Science, 2024-)

• Farahnaz Maroof, MS (Health Data Engineer, 2024-)

• Mounika Thakkallapally, MS (Health Data Engineer, 2024-)

• Paul Stey, PhD (Assistant CIO of Research Software Engineering and Data Science, 2017-)

• Ashok Ragavendran, PhD (Assistant Director of Computational Biology and Data Science, 2023-)

• Chris Kourkoulakos (Data Science Fellow - Python, 2024)

• Thomas Leonard (Data Science Fellow - R, 2024)

Former Contributors

• Aaron Eisman, MD, PhD (Co-Editor, 2020-2023)

• Katie Brown, MSN, RN, PhD (Co-Editor, 2020-2023)

• Dilum Aluthge, PhD, MD Candidate (Co-Editor, 2020-2023)

• Sotiris Karagounis (Research Assistant - Python Basics, 2020)

• My Vu (Research Assistant - R Basics, 2020)

• Elizabeth Song (Data Science Fellow - ML, 2021)

• Zach Sickles (Data Science Fellow, 2023)

• Kaitlyn Williams (Data Science Fellow, 2023)

• About

  • Introduction

  • Table of Contents

  • Contributors

  • Releases

• Foundations

  • Introduction

  • Biomedical Informatics

  • Implementation Science

  • Learning Health Systems

  • Health Data

  • Health Data Standards

  • Artificial Intelligence in Health

  • Observational Health Research

  • Health Research Data Governance

• Observational Research with EHR Data

  • Introduction

  • Best Practices

  • Research Pipeline

  • Communities, Platforms, and Organizations

• Computing Skills

  • Introduction

  • File Directory Structures

  • Text Editors

  • GitHub

  • Unix

  • Julia

  • Python

  • R

• URSA Initiative in Rhode Island

  • Introduction

  • Computing Environments

  • Health Data Partners

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.

Feedback

Feedback and corrections are welcome, which can be sent directly to URSA Services and Support (ursa-help@brown.edu).

Acknowledgments

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.