Advances of Machine Learning for Knowledge Mining in Electronic Health Records

By: P. Mohamed Fathimal (Editor), T. Ganesh Kumar (Editor), J. B. Shajilin Loret (Editor)

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Hardcover | 6 March 2025

At a Glance

Hardcover
286 Pages

Dimensions(cm)
23.39 x 15.6 x 1.75

Hardcover

RRP $315.00

$261.90

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The book explores the application of cutting-edge machine learning and deep learning algorithms in mining Electronic Health Records (EHR). With the aim of improving patient health management, this book explains the structure of EHR, consisting of demographics, medical history, and diagnosis, with a focus on the design and representation of structured, semi-structured, and unstructured data.

Introduces the design, organized, semi-structured, unstructured, and irregular time series data of electronic health records
Covers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured data.
Discusses supervised and unsupervised learning in electronic health records
Describes clustering and classification techniques for organized, semi-structured, and unstructured data from electronic health records

This book is an essential resource for researchers and professionals in fields like computer science, biomedical engineering, and information technology, seeking to enhance healthcare efficiency, security, and privacy through advanced data analytics and machine learning.

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