Atnaujinkite slapukų nuostatas

Multimodal AI in Healthcare: A Paradigm Shift in Health Intelligence 2023 ed. [Minkštas viršelis]

Edited by , Edited by , Edited by
  • Formatas: Paperback / softback, 416 pages, aukštis x plotis: 235x155 mm, weight: 670 g, 91 Illustrations, color; 10 Illustrations, black and white; XXII, 416 p. 101 illus., 91 illus. in color., 1 Paperback / softback
  • Serija: Studies in Computational Intelligence 1060
  • Išleidimo metai: 29-Nov-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031147731
  • ISBN-13: 9783031147739
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 416 pages, aukštis x plotis: 235x155 mm, weight: 670 g, 91 Illustrations, color; 10 Illustrations, black and white; XXII, 416 p. 101 illus., 91 illus. in color., 1 Paperback / softback
  • Serija: Studies in Computational Intelligence 1060
  • Išleidimo metai: 29-Nov-2023
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3031147731
  • ISBN-13: 9783031147739
Kitos knygos pagal šią temą:
This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical
Text by Leveraging External Knowledge.- Customized Training of Pretrained
Language Models to Detect Post Intents in Online Health Support
Groups.- EXPECT-NLP: An Integrated Pipeline and User Interface for Exploring
Patient Preferences Directly from Patient-Generated Text.