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Prognostic models in healthcareAI and statistical approaches /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Prognostic models in healthcareedited by Tanzila Saba, Amjad Rehman, Sudipta Roy.
其他題名:	AI and statistical approaches /
其他作者:	Saba, Tanzila.
出版者:	Singapore :Springer Nature Singapore :2022.
面頁冊數:	xxii, 504 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	DiagnosisData processing.
電子資源:	https://doi.org/10.1007/978-981-19-2057-8
ISBN:	9789811920578$q(electronic bk.)

Prognostic models in healthcareAI and statistical approaches /
Prognostic models in healthcareAI and statistical approaches /[electronic resource] :edited by Tanzila Saba, Amjad Rehman, Sudipta Roy. - Singapore :Springer Nature Singapore :2022. - xxii, 504 p. :ill. (chiefly color), digital ;24 cm. - Studies in big data,v. 1092197-6511 ;. - Studies in big data ;v.1..

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$q(electronic bk.)

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

611662
Diagnosis
--Data processing.

LC Class. No.: RC78.7.D35 / P76 2022

Dewey Class. No.: 616.0750285

Prognostic models in healthcareAI and statistical approaches /
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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.
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