Saba T Prognostic Models in Healthcare AI and Statistical Approaches 2022
Category: eBooks
- views: 100
- date: 16 July 2024
- posted by: BeMyLove
Saba T Prognostic Models in Healthcare AI and Statistical Approaches 2022 | 12.37 MB
515 Pages
Title: Prognostic Models in Healthcare: AI and Statistical Approaches
Author: Tanzila Saba (Editor), Amjad Rehman (Editor), Sudipta Roy (Editor)
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/7ad68396b2e033d107be8916b4c7b112/Saba_T._Prognostic_Models_in_Healthcare._AI_and_Statistical_Approaches_2022.rar
https://k2s.cc/file/bb0cd5dbddd5f/Saba_T._Prognostic_Models_in_Healthcare._AI_and_Statistical_Approaches_2022.rar
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.
DOWNLOAD:
https://rapidgator.net/file/7ad68396b2e033d107be8916b4c7b112/Saba_T._Prognostic_Models_in_Healthcare._AI_and_Statistical_Approaches_2022.rar
https://k2s.cc/file/bb0cd5dbddd5f/Saba_T._Prognostic_Models_in_Healthcare._AI_and_Statistical_Approaches_2022.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||