Advances machine learning knowledge (34 résultats)

Paperback. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : New. pp. 744.

Etat : New.

Etat : New.

Etat : New. pp. 744.

Etat : New. pp. 744.

Etat : New. In English.

hardcover. Etat : Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

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

Etat : 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.

hardcover. Etat : Fine.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Taschenbuch. Etat : 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. …

Hardcover. Etat : New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Hardcover. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New. In English.

Hardback. Etat : New. 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. …

Etat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Etat : Brand New. 304 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Etat : New. 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. …

N.A. Etat : New. ISBN:9781032526102.

Paperback. Etat : 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. …

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Etat : 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. …