02398 a2200493 4500 1032526106 20250328151419.0 250324022025xx 258 eng 9781032526102 BB Taylor & Francis GBP 150.00 BB 01 eng UYQ thema MQW thema UB thema UYF thema ULR thema TJFM thema UMZ thema UYQ bic MQW bic UB bic UYF bic ULR bic TJFM bic UMZ bic COM093000 bisac COM014000 bisac COM059000 bisac COM018000 bisac COM095000 bisac COM091000 bisac 006.31 bisac P. Mohamed Fathimal Advances of Machine Learning for Knowledge Mining in Electronic Health Records 1 Chapman and Hall/CRC 20250307 270 p 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. T. Ganesh Kumar B01 J. B. Shajilin Loret B01 Venkataraman Lakshmi B01 Manish T. I. B01 8005 8005