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Health informatics = a computational...

Patgiri, Ripon.

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Health informatics = a computational perspective in healthcare /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Health informatics/ edited by Ripon Patgiri, Anupam Biswas, Pinki Roy.
其他題名:	a computational perspective in healthcare /
其他作者:	Patgiri, Ripon.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	x, 377 p. :ill. (some col.), digital ;24 cm.
內容註:	6G Communication Technology: A Vision on Intelligent Healthcare -- Deep Learning Based Medical Image Analysis Using Transfer Learning -- Wearable Internet of Things for Personalized Healthcare: Study of Trends and Latent Research -- Principal Component Analysis, Quantifying, and Filtering of Poincare Plots for Time Series Typal For E-Health -- Medical Image Generation Using Generative Adversarial Networks: A Review -- Comparative Analysis of Various Deep Learning Algorithms for Diabetic Retinopathy Images -- Software Design Specification and Analysis of Insulin Dose to Adaptive Carbohydrate Algorithm for Type 1 Diabetic Patients -- Iot Based Healthcare Monitoring System Using 5G Communication & Machine Learning Models -- Medical Image Classification Techniques and Analysis Using Deep Learning Networks: A Review.
Contained By:	Springer Nature eBook
標題:	Medical informatics. -
電子資源:	https://doi.org/10.1007/978-981-15-9735-0
ISBN:	9789811597350

Health informatics = a computational perspective in healthcare /
Health informaticsa computational perspective in healthcare /[electronic resource] :edited by Ripon Patgiri, Anupam Biswas, Pinki Roy. - Singapore :Springer Singapore :2021. - x, 377 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.9321860-949X ;. - Studies in computational intelligence ;v.932..

6G Communication Technology: A Vision on Intelligent Healthcare -- Deep Learning Based Medical Image Analysis Using Transfer Learning -- Wearable Internet of Things for Personalized Healthcare: Study of Trends and Latent Research -- Principal Component Analysis, Quantifying, and Filtering of Poincare Plots for Time Series Typal For E-Health -- Medical Image Generation Using Generative Adversarial Networks: A Review -- Comparative Analysis of Various Deep Learning Algorithms for Diabetic Retinopathy Images -- Software Design Specification and Analysis of Insulin Dose to Adaptive Carbohydrate Algorithm for Type 1 Diabetic Patients -- Iot Based Healthcare Monitoring System Using 5G Communication & Machine Learning Models -- Medical Image Classification Techniques and Analysis Using Deep Learning Networks: A Review.

This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.

ISBN: 9789811597350

Standard No.: 10.1007/978-981-15-9735-0doiSubjects--Topical Terms:

661258
Medical informatics.

LC Class. No.: R859.7.D35 / H435 2021

Dewey Class. No.: 610.285

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520 $a This book presents innovative research works to demonstrate the potential and the advancements of computing approaches to utilize healthcare centric and medical datasets in solving complex healthcare problems. Computing technique is one of the key technologies that are being currently used to perform medical diagnostics in the healthcare domain, thanks to the abundance of medical data being generated and collected. Nowadays, medical data is available in many different forms like MRI images, CT scan images, EHR data, test reports, histopathological data and doctor patient conversation data. This opens up huge opportunities for the application of computing techniques, to derive data-driven models that can be of very high utility, in terms of providing effective treatment to patients. Moreover, machine learning algorithms can uncover hidden patterns and relationships present in medical datasets, which are too complex to uncover, if a data-driven approach is not taken. With the help of computing systems, today, it is possible for researchers to predict an accurate medical diagnosis for new patients, using models built from previous patient data. Apart from automatic diagnostic tasks, computing techniques have also been applied in the process of drug discovery, by which a lot of time and money can be saved. Utilization of genomic data using various computing techniques is another emerging area, which may in fact be the key to fulfilling the dream of personalized medications. Medical prognostics is another area in which machine learning has shown great promise recently, where automatic prognostic models are being built that can predict the progress of the disease, as well as can suggest the potential treatment paths to get ahead of the disease progression.
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