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Intelligent healthcare = application...

Bhatia, Surbhi.

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Intelligent healthcare = applications of AI in eHealth /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent healthcare/ edited by Surbhi Bhatia ... [et al.].
Reminder of title:	applications of AI in eHealth /
other author:	Bhatia, Surbhi.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xii, 323 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Part -I Medical Expert Systems -- Mobile Agents in Healthcare -- Fuzzy Expert Systems in Medical Domain -- Expert Systems for Bioinformatics -- Virtual Reality and Augmented Realty support systems -- Brain Computer Interface for rehabilitation -- Robotics in healthcare -- Part -II Machine Learning in Healthcare -- Rule based Learning in Healthcare -- Classification and Regression in Healthcare -- Clustering algorithms -- Deep Learning in Healthcare -- Big Data -- Nature Inspired Algorithms -- Part III Case Studies -- Corona Virus Study -- Dementia Prediction -- Brain Injury Data Analysis -- Pesticide data Analysis for cancer detection -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications. -
Online resource:	https://doi.org/10.1007/978-3-030-67051-1
ISBN:	9783030670511

Intelligent healthcare = applications of AI in eHealth /
Intelligent healthcareapplications of AI in eHealth /[electronic resource] :edited by Surbhi Bhatia ... [et al.]. - Cham :Springer International Publishing :2021. - xii, 323 p. :ill., digital ;24 cm. - EAI/Springer innovations in communication and computing,2522-8595. - EAI/Springer innovations in communication and computing..

Introduction -- Part -I Medical Expert Systems -- Mobile Agents in Healthcare -- Fuzzy Expert Systems in Medical Domain -- Expert Systems for Bioinformatics -- Virtual Reality and Augmented Realty support systems -- Brain Computer Interface for rehabilitation -- Robotics in healthcare -- Part -II Machine Learning in Healthcare -- Rule based Learning in Healthcare -- Classification and Regression in Healthcare -- Clustering algorithms -- Deep Learning in Healthcare -- Big Data -- Nature Inspired Algorithms -- Part III Case Studies -- Corona Virus Study -- Dementia Prediction -- Brain Injury Data Analysis -- Pesticide data Analysis for cancer detection -- Conclusion.

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniques and deploying novel technologies in intelligent healthcare services and applications. Describes the advances of computing methodologies for life and medical science data; Presents applications of artificial intelligence in healthcare along with case studies and datasets; Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

ISBN: 9783030670511

Standard No.: 10.1007/978-3-030-67051-1doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.28563

Intelligent healthcare = applications of AI in eHealth /
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505 0 $a Introduction -- Part -I Medical Expert Systems -- Mobile Agents in Healthcare -- Fuzzy Expert Systems in Medical Domain -- Expert Systems for Bioinformatics -- Virtual Reality and Augmented Realty support systems -- Brain Computer Interface for rehabilitation -- Robotics in healthcare -- Part -II Machine Learning in Healthcare -- Rule based Learning in Healthcare -- Classification and Regression in Healthcare -- Clustering algorithms -- Deep Learning in Healthcare -- Big Data -- Nature Inspired Algorithms -- Part III Case Studies -- Corona Virus Study -- Dementia Prediction -- Brain Injury Data Analysis -- Pesticide data Analysis for cancer detection -- Conclusion.
520 $a This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniques and deploying novel technologies in intelligent healthcare services and applications. Describes the advances of computing methodologies for life and medical science data; Presents applications of artificial intelligence in healthcare along with case studies and datasets; Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.
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