£125.00

Springer Deep Learning for Biomedical and Health Informatics

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Description

Deep Learning Techniques for Biomedical and Health Informatics provides a comprehensive look at modern approaches for healthcare-related applications. As healthcare informatics seeks to improve treatment quality and patient lives, the ability to analyze massive amounts of biomedical data becomes essential. This volume, part of the Studies in Big Data series, focuses on the efficient analysis of diverse datasets, including electronic health records (EHRs), patient data, and lifestyle information. While traditional methods often relied on domain experts to manually develop models, recent advancements in data representation have changed the landscape. This book explores how deep learning can be applied to handle the abundance of healthcare data to ensure high-quality and efficient care. It is an important resource for those studying the intersection of artificial intelligence and medical informatics, offering insights into how machine learning models can transform complex health data into actionable medical knowledge.

Key Features

Explores state-of-the-art deep learning approaches specifically designed for biomedical and health-related applications.

Addresses the challenges of analyzing abundant healthcare data, including electronic health records (EHRs) and patient information.

Provides insights into improving healthcare efficiency and treatment quality through advanced data analysis techniques.

Covers a wide range of data types, from clinical patient records to lifestyle-related health problems.

Part of the Studies in Big Data series, offering specialized knowledge in the field of healthcare informatics.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
25 November 2019
Listed Since
21 September 2019

Barcode

No barcode data available

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