Statistics and machine learning methods for EHR data : from data extraction to data analytics
edited by Hulin Wu, Jose Miguel Yamal, Ashraf Yaseen, and Vahed Maroufy
Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- About the Editors -- Contributors -- 1. Introduction: Use of EHR Data for Scientific Discoveries--Challenges and Opportunities -- 1.1. Real-World Data and Real-World Evidence: Big Data in Practice -- 1.2. Use of EMR/EHR Database for Research and Scientific Discoveries: Procedure and Life Cycle -- 1.2.1. Initiate a Project -- 1.2.2. Data Queries and Data Extraction -- 1.2.3. Data Cleaning -- 1.2.4. Data Pre-Processing or Processing -- 1.2.5. Data Preparation -- 1.2.6. Data Analysis, Modeling and Prediction -- 1.2.7. Result Validation -- 1.2.8. Result Interpretation -- 1.2.9. Publication and Dissemination -- 1.3. Challenges and Opportunities -- References -- 2. EHR Project Management -- 2.1. Introduction -- 2.1.1. What is Project Management? -- 2.1.2. Why We Need Project Management? -- 2.1.3. Project Management Goals and Principles -- 2.2. Project and Sub-Project in EHR Research -- 2.3. Data, Code and Product Management -- 2.3.1. Data Loss Prevention -- 2.3.2. Naming Conventions -- 2.3.3. Version Control -- 2.3.4. Coding Convention -- Object-Oriented or Non-Object-Oriented Programming -- 2.3.5. Document Management: Data Analysis Report, Papers and Read-Me Documents -- 2.4. Team/People Management -- 2.4.1. How to Form a Team: What Expertise is Needed for EHR Projects? -- 2.4.2. How to Efficiently Manage a Multidisciplinary Team? -- 2.4.3. Task Management -- 2.5. Management Methods and Software Tools -- 2.6. An Example of a Data Management Framework -- 2.6.1. Folder Management -- Naming -- Structure -- Main Folders -- CBD_HS -- Public_Folder -- Admin -- Useful_Info -- Group Folders -- Project Folders -- Sub_Project Folders -- 2.6.2. File Management -- Naming -- Structure -- File Submission -- 2.6.3. User Management -- User Groups.
