東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Multimodal ai in healthcare = a para...

International Workshop on Health Intelligence ((2022))

Linked to FindBook

Google Book

Amazon

博客來

Multimodal ai in healthcare = a paradigm shift in health intelligence /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal ai in healthcare/ edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco.
Reminder of title:	a paradigm shift in health intelligence /
other author:	Shaban-Nejad, Arash.
corporate name:	International Workshop on Health Intelligence
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xxii, 416 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Medical applications -
Online resource:	https://doi.org/10.1007/978-3-031-14771-5
ISBN:	9783031147715

Multimodal ai in healthcare = a paradigm shift in health intelligence /
Multimodal ai in healthcarea paradigm shift in health intelligence /[electronic resource] :edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco. - Cham :Springer International Publishing :2023. - xxii, 416 p. :ill. (chiefly color), digital ;24 cm. - Studies in computational intelligence,v. 10601860-9503 ;. - Studies in computational intelligence ;v. 1060..

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.

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.

ISBN: 9783031147715

Standard No.: 10.1007/978-3-031-14771-5doiSubjects--Topical Terms:

660945
Artificial intelligence
--Medical applications

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.285

Multimodal ai in healthcare = a paradigm shift in health intelligence /
LDR:02456nmm a2200337 a 4500 001 2314933
003 DE-He213
005 20221128042016.0
006 m d
007 cr nn 008maaau
008 230902s2023 sz s 0 eng d
020 $a 9783031147715 $q (electronic bk.)
020 $a 9783031147708 $q (paper)
024 7 $a 10.1007/978-3-031-14771-5 $2 doi
035 $a 978-3-031-14771-5
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a R859.7.A78
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 610.285 $2 23
090 $a R859.7.A78 $b I61 2022
111 2 $a International Workshop on Health Intelligence $d (2022) $3 3626846
245 1 0 $a Multimodal ai in healthcare $h [electronic resource] : $b a paradigm shift in health intelligence / $c edited by Arash Shaban-Nejad, Martin Michalowski, Simone Bianco.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xxii, 416 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1060
505 0 $a 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.
520 $a 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.
650 0 $a Artificial intelligence $x Medical applications $x Congresses. $3 660945
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Biomedical Engineering and Bioengineering. $3 3381533
650 2 4 $a Data Engineering. $3 3409361
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Shaban-Nejad, Arash. $3 3258638
700 1 $a Michalowski, Martin. $3 3443413
700 1 $a Bianco, Simone. $3 3226651
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1060. $3 3626847
856 4 0 $u https://doi.org/10.1007/978-3-031-14771-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)