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Advanced prognostic predictive model...
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Goyal, Lalit Mohan.
Advanced prognostic predictive modelling in healthcare data analytics
Record Type:
Electronic resources : Monograph/item
Title/Author:
Advanced prognostic predictive modelling in healthcare data analyticsedited by Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal.
other author:
Roy, Sudipta.
Published:
Singapore :Springer Singapore :2021.
Description:
xiv, 311 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
Artificial intelligenceMedical applications.
Online resource:
https://doi.org/10.1007/978-981-16-0538-3
ISBN:
9789811605383$q(electronic bk.)
Advanced prognostic predictive modelling in healthcare data analytics
Advanced prognostic predictive modelling in healthcare data analytics
[electronic resource] /edited by Sudipta Roy, Lalit Mohan Goyal, Mamta Mittal. - Singapore :Springer Singapore :2021. - xiv, 311 p. :ill. (some col.), digital ;24 cm. - Lecture notes on data engineering and communications technologies,v.642367-4512 ;. - Lecture notes on data engineering and communications technologies ;v.2..
Data visualization in the transformation of healthcare industries -- Emerging Healthcare Problems in High Dimensional Data and Dimension Reduction -- Disease Prediction Using Data Mining and Machine Learning Techniques -- Prognostic modeling with the internet of Healthcare things applications -- Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach -- Early Screening of COVID-19 from Chest CT using Deep Learning Technique -- Clinical Decision-Making and Predicting Patient Trajectories.
This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
ISBN: 9789811605383$q(electronic bk.)
Standard No.: 10.1007/978-981-16-0538-3doiSubjects--Topical Terms:
237917
Artificial intelligence
--Medical applications.
LC Class. No.: R859.7.A78 / A383 2021
Dewey Class. No.: 610.28563
Advanced prognostic predictive modelling in healthcare data analytics
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Data visualization in the transformation of healthcare industries -- Emerging Healthcare Problems in High Dimensional Data and Dimension Reduction -- Disease Prediction Using Data Mining and Machine Learning Techniques -- Prognostic modeling with the internet of Healthcare things applications -- Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach -- Early Screening of COVID-19 from Chest CT using Deep Learning Technique -- Clinical Decision-Making and Predicting Patient Trajectories.
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This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.
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Intelligent Technologies and Robotics (SpringerNature-42732)
based on 0 review(s)
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電子館藏
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電子館藏
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EB R859.7.A78 A244 2021 2021
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1 records • Pages 1 •
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https://doi.org/10.1007/978-981-16-0538-3
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