Advances of Machine Learning for Knowledge Mining in Electronic Health Records

Advances of Machine Learning for Knowledge Mining in Electronic Health Records
Format
Hardback
Publisher
Taylor & Francis Ltd
Country
United Kingdom
Published
3 March 2025
Pages
304
ISBN
9781032526102

Advances of Machine Learning for Knowledge Mining in Electronic Health Records

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.

Order online and we’ll ship when available (3 March 2025)

Our stock data is updated periodically, and availability may change throughout the day for in-demand items. Please call the relevant shop for the most current stock information. Prices are subject to change without notice.

Sign in or become a Readings Member to add this title to a wishlist.