Statistics and Machine Learning Methods for EHR Data From Data Extraction to Data Analytics.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Wu, Hulin
Otros Autores: Yamal, Jose Miguel, Yaseen, Ashraf, Maroufy, Vahed
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Milton : CRC Press LLC, 2020.
Colección:Chapman and Hall/CRC Healthcare Informatics Ser.
Temas:
Medical records > Data processing.
Dossiers médicaux > Informatique.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 2.6.4. Data Management Framework
  • 2.7. Discussion and Summary
  • 2.8. Appendix
  • File Submission Form
  • Note
  • References
  • 3. EHR Databases and Data Management: Data Query and Extraction
  • 3.1. Introduction
  • 3.2. EHR/EMR Database Availability and Access
  • 3.3. EHR/EMR Database Design and Structure: Database Queries
  • 3.3.1. Database Construction
  • 3.3.2. Traditional Relational Database System
  • 3.3.3. Distributed Database System
  • 3.4. Data Extraction
  • 3.4.1. Define Inclusion/Exclusion Criteria for Data Extraction
  • 3.4.2. Phenotyping: Cohort Identification
  • 3.5. Data Extraction Report
  • 3.6. Illustration Example: Subarachnoid Hemorrhage (SAH) Project
  • 3.6.1. EHR Database Design and Construction
  • 3.6.2. SAH Cohort Identification and Data Extraction
  • 3.6.3. Data Extraction Report
  • 3.6.4. Potential Data Extraction Pitfalls and Errors with Solutions
  • References
  • 4. EHR Data Cleaning
  • 4.1. Introduction
  • 4.2. Review of Current Data Cleaning Methods and Tools
  • 4.2.1. Data Wranglers

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