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. 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.

Pages: 270, Edition: 1, Paperback, Chapman and Hall/CRC