Cargando…

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:
Acceso en línea:Texto completo

MARC

LEADER 00000cam a2200000Mu 4500
001 EBOOKCENTRAL_on1202474939
003 OCoLC
005 20240329122006.0
006 m o d
007 cr |||||||||||
008 201031s2020 xx o ||| 0 eng d
040 |a EBLCP  |b eng  |c EBLCP  |d YDX  |d OCLCQ  |d REDDC  |d OCLCO  |d OCLCL 
019 |a 1202230253  |a 1202469259 
020 |a 9781000260946 
020 |a 1000260941 
020 |z 0367442396 
020 |z 9780367442392 
035 |a (OCoLC)1202474939  |z (OCoLC)1202230253  |z (OCoLC)1202469259 
050 4 |a R864  |b .S738 2021 
082 0 4 |a 610.285  |q OCoLC  |2 23/eng/20231120 
049 |a UAMI 
100 1 |a Wu, Hulin. 
245 1 0 |a Statistics and Machine Learning Methods for EHR Data  |h [electronic resource] :  |b From Data Extraction to Data Analytics. 
260 |a Milton :  |b CRC Press LLC,  |c 2020. 
300 |a 1 online resource (329 p.). 
490 1 |a Chapman and Hall/CRC Healthcare Informatics Ser. 
500 |a Description based upon print version of record. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
500 |a 4.2.2. Data Cleaning Tools for Specific EHR Datasets. 
590 |a ProQuest Ebook Central  |b Ebook Central Academic Complete 
650 0 |a Medical records  |x Data processing. 
650 6 |a Dossiers médicaux  |x Informatique. 
700 1 |a Yamal, Jose Miguel. 
700 1 |a Yaseen, Ashraf. 
700 1 |a Maroufy, Vahed. 
758 |i has work:  |a Statistics and machine learning methods for EHR data (Text)  |1 https://id.oclc.org/worldcat/entity/E39PCG3HHPkRtqHyHHbKQRX9cK  |4 https://id.oclc.org/worldcat/ontology/hasWork 
776 0 8 |i Print version:  |a Wu, Hulin  |t Statistics and Machine Learning Methods for EHR Data : From Data Extraction to Data Analytics  |d Milton : CRC Press LLC,c2020  |z 9780367442392 
830 0 |a Chapman and Hall/CRC Healthcare Informatics Ser. 
856 4 0 |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6378532  |z Texto completo 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL6378532 
938 |a YBP Library Services  |b YANK  |n 17076178 
994 |a 92  |b IZTAP