國立虎尾科技大學 |

Prognostic Models in Healthcare: AI and Statistical Approaches

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Prognostic Models in Healthcare: AI and Statistical Approaches/ edited by Tanzila Saba, Amjad Rehman, Sudipta Roy.
other author:	Saba, Tanzila.
Description:	XXII, 504 p. 211 illus., 161 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-981-19-2057-8
ISBN:	9789811920578

Prognostic Models in Healthcare: AI and Statistical Approaches
Prognostic Models in Healthcare: AI and Statistical Approaches[electronic resource] /edited by Tanzila Saba, Amjad Rehman, Sudipta Roy. - 1st ed. 2022. - XXII, 504 p. 211 illus., 161 illus. in color.online resource. - Studies in Big Data,1092197-6511 ;. - Studies in Big Data,8.

Segmentation of White Blood Cells in Acute Myeloid Leukaemia Microscopic Images: The Current Challenges and Future Solutions -- Computer Vision Based Prognostic Modeling of COVID-19 from Medical Imaging -- Skin Lesion Classification From Dermoscopic Images with Deep Residual Network based Fused Pigmented Deep Feature Extraction and Entropy Based Best Features Selection Approach -- Computer Vision Technologies for COVID-19 Prediction, Diagnosis and Prevention -- Health monitoring methods in heart diseases based on data mining approach, a directional survey -- Machine learning based brain diseases diagnosing in electroencephalogram signals, Alzheimer and Parkinson's -- Skin Lesion Detection Using Recent Machine Learning Approaches -- Improving monitoring and controling parameters for Alzheimer's patients based on IoT -- A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network -- Leukemia Detection Using Machine and Deep Learning Through Microscopic Images-A Review.

This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.

ISBN: 9789811920578

Standard No.: 10.1007/978-981-19-2057-8doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Prognostic Models in Healthcare: AI and Statistical Approaches
LDR:03889nam a22004095i 4500 001 1088291
003 DE-He213
005 20220706073314.0
007 cr nn 008mamaa
008 221228s2022 si | s |||| 0|eng d
020 $a 9789811920578 $9 978-981-19-2057-8
024 7 $a 10.1007/978-981-19-2057-8 $2 doi
035 $a 978-981-19-2057-8
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Prognostic Models in Healthcare: AI and Statistical Approaches $h [electronic resource] / $c edited by Tanzila Saba, Amjad Rehman, Sudipta Roy.
250 $a 1st ed. 2022.
264 1 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2022.
300 $a XXII, 504 p. 211 illus., 161 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Big Data, $x 2197-6511 ; $v 109
505 0 $a Segmentation of White Blood Cells in Acute Myeloid Leukaemia Microscopic Images: The Current Challenges and Future Solutions -- Computer Vision Based Prognostic Modeling of COVID-19 from Medical Imaging -- Skin Lesion Classification From Dermoscopic Images with Deep Residual Network based Fused Pigmented Deep Feature Extraction and Entropy Based Best Features Selection Approach -- Computer Vision Technologies for COVID-19 Prediction, Diagnosis and Prevention -- Health monitoring methods in heart diseases based on data mining approach, a directional survey -- Machine learning based brain diseases diagnosing in electroencephalogram signals, Alzheimer and Parkinson's -- Skin Lesion Detection Using Recent Machine Learning Approaches -- Improving monitoring and controling parameters for Alzheimer's patients based on IoT -- A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network -- Leukemia Detection Using Machine and Deep Learning Through Microscopic Images-A Review.
520 $a This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.
650 0 $a Computational intelligence. $3 568984
650 0 $a Medical informatics. $3 583858
650 0 $a Computer simulation. $3 560190
650 0 $a Quantitative research. $3 635913
650 0 $a Neural networks (Computer science) . $3 1253765
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Computer Modelling. $3 1365730
650 2 4 $a Data Analysis and Big Data. $3 1366136
650 2 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 884110
700 1 $a Saba, Tanzila. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1395444
700 1 $a Rehman, Amjad. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1395445
700 1 $a Roy, Sudipta. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1323928
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811920561
776 0 8 $i Printed edition: $z 9789811920585
776 0 8 $i Printed edition: $z 9789811920592
830 0 $a Studies in Big Data, $x 2197-6503 ; $v 8 $3 1256918
856 4 0 $u https://doi.org/10.1007/978-981-19-2057-8
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)