Shulph Ink
Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics
Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics
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Excellent- More about Statistics and Machine Learning Methods for EHR Data: From Data Extraction to Data Analytics
This book discusses the use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data for research, covering topics such as data extraction, cleaning, processing, analysis, inference, and predictions. It evaluates and compares standard statistical models and machine learning and deep learning methods for predicting clinical outcomes based on EHR data, highlighting the importance of multidisciplinary collaborations.
Format: Hardback
Length: 313 pages
Publication date: 16 December 2020
Publisher: Taylor & Francis Ltd
The utilization of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data for research is on the rise, presenting unique challenges due to its collection, processing, and the types of questions that can be addressed. This comprehensive book delves into various essential topics related to leveraging EHR/EMR data for research, encompassing data extraction, cleaning, processing, analysis, inference, and predictions based on the authors' extensive practical experience.
The book meticulously evaluates and compares standard statistical models and approaches with those of machine learning and deep learning methods, presenting unbiased comparison results for these methods in predicting clinical outcomes based on EHR data.
Key Features:
Written by contributors from multidisciplinary EHR research projects, encompassing methods and approaches from statistics, computing, informatics, data science, and clinical/epidemiological domains.
Documented detailed experiences on EHR data extraction, cleaning, and preparation, providing a comprehensive view of statistical approaches and machine learning prediction models to address the challenges and limitations of EHR data.
Considers the entire cycle of EHR data analysis, reflecting the multidisciplinary perspective required for successful EHR/EMR analysis.
The use of EHR/EMR analysis necessitates close collaboration between statisticians, informaticians, data scientists, and clinical/epidemiological investigators. This book exemplifies that collaborative approach.
Weight: 638g
Dimension: 162 x 242 x 25 (mm)
ISBN-13: 9780367442392
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