Isbn: 9781032527819 - advances of machine learning for knowledge mining in electronic health records (8 resultados)

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: Brand New. 270 pages. 6.14x0.60x9.21 inches. In Stock.

Condición: New. P. Mohamed Fathimal is working as an Assistant Professor in the Department of Computer Science and Engineering, Anna University. She received her PhD, ME, and BE in Computer Science and Engineering from Manonmaniam Sundaranar University, Tirunelve.

Taschenbuch. Condición: Neu. Neuware - 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. …

Paperback. Condición: new. Paperback. 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 recordsCovers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured dataDiscusses supervised and unsupervised learning in electronic health recordsDescribes clustering and classification techniques for organized, semi- structured, and unstructured data from electronic health recordsThis 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Paperback. Condición: new. Paperback. 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 recordsCovers information extraction, standards for meta-data, reuse of metadata for clinical research, and organized and unstructured dataDiscusses supervised and unsupervised learning in electronic health recordsDescribes clustering and classification techniques for organized, semi- structured, and unstructured data from electronic health recordsThis 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …