Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics (Chapman & Hall/CRC Healthcare Informatics Series)

By: and and and

Sign Up Now! Already a Member? Log In
You must be logged into UK education collection to access this title. Learn about membership options, or view our freely available titles.

Synopsis
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.
Copyright:
2021

Book Details

Book Quality:
Publisher Quality
Book Size:
313 Pages
ISBN-13:
9781000260960
Related ISBNs:
9780367442392, 9781003030003, 9780367638399
Publisher:
CRC Press
Date of Addition:
Copyrighted By:
Taylor
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Business and Finance, Mathematics and Statistics, Medicine
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.
Edited by:
Hulin Wu
Edited by:
Jose Miguel Yamal
Edited by:
Ashraf Yaseen
Edited by:
Vahed Maroufy

Reviews

No Rating Yet