9783110708004-3110708000-Deep Learning for Personalized Healthcare Services (Intelligent Biomedical Data Analysis, 7)

Deep Learning for Personalized Healthcare Services (Intelligent Biomedical Data Analysis, 7)

ISBN-13: 9783110708004
ISBN-10: 3110708000
Edition: 1
Author: Chatterjee, Jain, Vishal, Hedayati, Hadi, Krit, Salahddine, Deperlioglu, Omer, Jyotir Moy
Publication date: 2021
Publisher: De Gruyter
Format: Hardcover 268 pages
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Book details

ISBN-13: 9783110708004
ISBN-10: 3110708000
Edition: 1
Author: Chatterjee, Jain, Vishal, Hedayati, Hadi, Krit, Salahddine, Deperlioglu, Omer, Jyotir Moy
Publication date: 2021
Publisher: De Gruyter
Format: Hardcover 268 pages

Summary

Deep Learning for Personalized Healthcare Services (Intelligent Biomedical Data Analysis, 7) (ISBN-13: 9783110708004 and ISBN-10: 3110708000), written by authors Chatterjee, Jain, Vishal, Hedayati, Hadi, Krit, Salahddine, Deperlioglu, Omer, Jyotir Moy, was published by De Gruyter in 2021. With an overall rating of 3.7 stars, it's a notable title among other AI & Machine Learning (Robotics, Hardware & DIY, Algorithms, Programming, History & Culture, Anthropology, Behavioral Sciences, Computer Science) books. You can easily purchase or rent Deep Learning for Personalized Healthcare Services (Intelligent Biomedical Data Analysis, 7) (Hardcover) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS
By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.

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