Advances of Machine Learning for Knowledge Mining in Electronic Health Records (1)

Synopsis

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. Explains the design of 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.

Copyright:

2025

Book Details

Book Quality:

Publisher Quality

Book Size:

284 Pages

ISBN-13:

9781040305744

Related ISBNs:

9781032526102, 9781040305690, 9781003408376

Publisher:

CRC Press

Date of Addition:

03/11/25

Copyrighted By:

selection and editorial matter, P. Mohamed Fathimal, T. Ganesh Kumar, J. B. Shajilin Loret, Venkataraman Lakshmi, Manish T. I.

Adult content:

No

Language:

English

Has Image Descriptions:

No

Categories:

Nonfiction, Computers and Internet

Submitted By:

Bookshare Staff

Usage Restrictions:

This is a copyrighted book.

Edited by:

P. Mohamed Fathimal

Edited by:

T. Ganesh Kumar

Edited by:

J. B. Loret

Edited by:

Venkataraman Lakshmi

Edited by:

Manish T. I.