Multimodal AI in healthcare : a paradigm shift in health intelligence / Arash Shaban-Nejad, Martin Michalowski, Simone Bianco, editors.
2023
R859.7.A78
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Details
Title
Multimodal AI in healthcare : a paradigm shift in health intelligence / Arash Shaban-Nejad, Martin Michalowski, Simone Bianco, editors.
Meeting Name
International Workshop on Health Intelligence (2022)
ISBN
9783031147715 (electronic bk.)
3031147715 (electronic bk.)
9783031147708
3031147707
3031147715 (electronic bk.)
9783031147708
3031147707
Published
Cham : Springer, [2023]
Copyright
©2023
Language
English
Description
1 online resource (xxii, 416 pages) : illustrations (chiefly color).
Other Standard Identifiers
10.1007/978-3-031-14771-5 doi
Call Number
R859.7.A78
Dewey Decimal Classification
610.285
Summary
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.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Meeting Name
AAAI Conference on Artificial Intelligence (36th : 2022)
Series
Studies in computational intelligence ; v. 1060.
Available in Other Form
Multimodal AI in healthcare.
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Online Access
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Table of Contents
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.
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.