Handbook of Artificial Intelligence in Healthcare

Vol. 1 - Advances and Applications
Herausgegeben:Lim, Chee-Peng; Vaidya, Ashlesha; Jain, Kiran; Mahorkar, Virag U.; Jain, Lakhmi C

• Gebundenes Buch

Produktbeschreibung

This handbook on Artificial Intelligence (AI) in healthcare consists of two volumes. The first volume is dedicated to advances and applications of AI methodologies in specific healthcare problems, while the second volume is concerned with general practicality issues and challenges and future prospects in the healthcare context.

The advent of digital and computing technologies has created a surge in the development of AI methodologies and their penetration to a variety of activities in our daily lives in recent years. Indeed, researchers and practitioners have designed and developed a variety of AI-based systems to help advance health and well-being of humans.

In this first volume, we present a number of latest studies in AI-based tools and techniques from two broad categories, viz., medical signal, image, and video processing as well as healthcare information and data analytics in Part 1 and Part 2, respectively. These selected studies offer readers practical knowledge and understanding pertaining to the recent advances and applications of AI in the healthcare sector.

Produktdetails

• Intelligent Systems Reference Library 211
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-79160-5
• 1st ed. 2022
• Seitenzahl: 476
• Erscheinungstermin: 18. September 2021
• Englisch
• Abmessung: 241mm x 160mm x 31mm
• Gewicht: 875g
• ISBN-13: 9783030791605
• ISBN-10: 3030791602
• Artikelnr.: 61896970

Autorenporträt

  

Inhaltsangabe

Advances in artificial intelligence for the identification of epileptiform discharges.- Characterizing EEG electrodes in directed functional brain networks using normalized transfer entropy and page rank.- Autistic verbal behaviour language parameterization.- Case studies to demonstrate real-world applications in ophthalmic image analysis.- Segmentation of petri plate images for automatic reporting of urine culture tests.- Repurposing routine imaging for cancer biomarker discovery using machine learning.