Statistics and Machine Learning Methods for EHR Data
From Data Extraction to Data Analytics
1st Edition
Published on 1. August 2022
Book
Paperback/Softback
313 pages
978-0-367-63839-9 (ISBN)
Description
The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data.
Key Features:
Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains.
Documents the detailed experience on EHR data extraction, cleaning and preparation
Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data.
Considers the complete cycle of EHR data analysis.
The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
Key Features:
Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains.
Documents the detailed experience on EHR data extraction, cleaning and preparation
Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data.
Considers the complete cycle of EHR data analysis.
The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
Reviews / Votes
'This book should make it to the bookshelf of anyone involved in data preparation and statistical analysis for EHR research.'- Madan G. Kandu, Journal of Biopharmaceutcal Statistics, Vol 31, No 4
'To conclude, this book provides a strong basis for handling real-world data from EHR and will be useful both for the beginner and for more advanced researchers.'
- Sebastien Bailly, International Society for Clinical Biostatistics, 72, 2021
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
73 s/w Abbildungen, 1 s/w Photographie bzw. Rasterbild, 72 s/w Zeichnungen, 29 s/w Tabellen
29 Tables, black and white; 72 Line drawings, black and white; 1 Halftones, black and white; 73 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 18 mm
Weight
500 gr
ISBN-13
978-0-367-63839-9 (9780367638399)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Hulin Wu | Jose Miguel Yamal | Ashraf Yaseen
Statistics and Machine Learning Methods for EHR Data
From Data Extraction to Data Analytics
Book
12/2020
1st Edition
Chapman & Hall/CRC
€186.70
Shipment within 15-20 days
Hulin Wu | Jose Miguel Yamal | Ashraf Yaseen
Statistics and Machine Learning Methods for EHR Data
From Data Extraction to Data Analytics
E-Book
12/2020
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Hulin Wu | Jose Miguel Yamal | Ashraf Yaseen
Statistics and Machine Learning Methods for EHR Data
From Data Extraction to Data Analytics
E-Book
12/2020
1st Edition
Chapman & Hall/CRC
€68.49
Available for download
Persons
Hulin Wu, PhD, the endowed Betty Wheless Trotter Professor and Chair, Department of Biostatistics & Data Science, School of Public Health (SPH), University of Texas Health Science Center at Houston (UTHealth). Dr. Wu also holds a joined appointment as Professor at UTHealth School of Biomedical Informatics. Dr. Wu received BS and MS training in engineering and PhD in statistics. He has many years of experience in developing novel statistical methods, mathematical models and informatics tools for biomedical data analysis and modeling. He is the Founding Director of the Center for Big Data in Health Sciences (CBD-HS) and he is directing the EHR research working group at UTHealth SPH.
Dr. Yamal is a tenured Associate Professor in the Department of Biostatistics & Data Science and a member of the Coordinating Center for Clinical Trials at UTHealth School of Public Health. Dr. Yamal has extensive experience in clinical trials including data coordinating centers and serving on Data Safety Monitoring Boards for clinical trials in stroke and traumatic brain injury. He has also contributed towards statistical methodology for classification problems for nested data as well as machine learning applications.
Ashraf Yaseen is an Assistant Professor of Data Science at the School of Public Health, UTHealth. He has extensive experience in database design, implementation and management, machine learning, and high-performance computing. In his current research work, Dr. Yaseen is exploring big data integration and deep learning technologies in electronic health records to address clinical and public health questions.
Vahed Maroufy, PhD, Assistant Professor, Department of Biostatistics & Data Science, UTHealth School of Public Health. Dr. Maroufy received MSc and PhD training in statistics and has experience in applied and theoretical statistics, including geometry of statistical models, mixture models, Bayesian inference, predictive models using EHR data, and analysis of genetic data in cancer research.
Dr. Yamal is a tenured Associate Professor in the Department of Biostatistics & Data Science and a member of the Coordinating Center for Clinical Trials at UTHealth School of Public Health. Dr. Yamal has extensive experience in clinical trials including data coordinating centers and serving on Data Safety Monitoring Boards for clinical trials in stroke and traumatic brain injury. He has also contributed towards statistical methodology for classification problems for nested data as well as machine learning applications.
Ashraf Yaseen is an Assistant Professor of Data Science at the School of Public Health, UTHealth. He has extensive experience in database design, implementation and management, machine learning, and high-performance computing. In his current research work, Dr. Yaseen is exploring big data integration and deep learning technologies in electronic health records to address clinical and public health questions.
Vahed Maroufy, PhD, Assistant Professor, Department of Biostatistics & Data Science, UTHealth School of Public Health. Dr. Maroufy received MSc and PhD training in statistics and has experience in applied and theoretical statistics, including geometry of statistical models, mixture models, Bayesian inference, predictive models using EHR data, and analysis of genetic data in cancer research.
Content
1. Introduction: Use of EHR Data for Research-Challenges and Opportunities. 2. EHR Project Management. 3. EHR Databases: Data Queries and Extraction. 4. EHR Data Cleaning. 5. EHR Data Pre-Processing and Preparation. 6. EHR Missing Data Issues. 7. Causal Inference and Analysis for EHR Data. 8. EHR Data Exploration, Analysis and Predictions: Statistical Models and Methods. 9. EHR Data Analytics and Predictions: Neural Network and Deep Learning Methods. 10. EHR Data Analytics and Predictions: Other Machine Learning Methods. 11. Use of EHR Data for Research: Future.