國立虎尾科技大學 |

Artificial Intelligence and Machine Learning in Public Healthcare = Opportunities and Societal Impact /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Artificial Intelligence and Machine Learning in Public Healthcare/ by KC Santosh, Loveleen Gaur.
其他題名:	Opportunities and Societal Impact /
作者:	Santosh, KC.
其他作者:	Gaur, Loveleen.
面頁冊數:	XXIII, 74 p. 30 illus., 14 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Public Health. -
電子資源:	https://doi.org/10.1007/978-981-16-6768-8
ISBN:	9789811667688

Artificial Intelligence and Machine Learning in Public Healthcare = Opportunities and Societal Impact /
Santosh, KC.

Artificial Intelligence and Machine Learning in Public HealthcareOpportunities and Societal Impact /[electronic resource] :by KC Santosh, Loveleen Gaur. - 1st ed. 2021. - XXIII, 74 p. 30 illus., 14 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3712. - SpringerBriefs in Computational Intelligence,.

Introduction to AI in Public Health -- Open Public Health Issues -- AI Solutions to Public Health Issues -- AI in Sustainable Public Healthcare -- AI in Precision Medicine -- Societal Impact: AI in Public Health Issues -- Case Studies - AI for Infectious Disease -- Privacy, Security and Ethical Issues.

This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.

ISBN: 9789811667688

Standard No.: 10.1007/978-981-16-6768-8doiSubjects--Topical Terms:

592982
Public Health.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Artificial Intelligence and Machine Learning in Public Healthcare = Opportunities and Societal Impact /
LDR:02915nam a22003975i 4500 001 1058043
003 DE-He213
005 20211126052352.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811667688 $9 978-981-16-6768-8
024 7 $a 10.1007/978-981-16-6768-8 $2 doi
035 $a 978-981-16-6768-8
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Santosh, KC. $e author. $0 (orcid)0000-0003-4176-0236 $1 https://orcid.org/0000-0003-4176-0236 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1363588
245 1 0 $a Artificial Intelligence and Machine Learning in Public Healthcare $h [electronic resource] : $b Opportunities and Societal Impact / $c by KC Santosh, Loveleen Gaur.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XXIII, 74 p. 30 illus., 14 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computational Intelligence, $x 2625-3712
505 0 $a Introduction to AI in Public Health -- Open Public Health Issues -- AI Solutions to Public Health Issues -- AI in Sustainable Public Healthcare -- AI in Precision Medicine -- Societal Impact: AI in Public Health Issues -- Case Studies - AI for Infectious Disease -- Privacy, Security and Ethical Issues.
520 $a This book discusses and evaluates AI and machine learning (ML) algorithms in dealing with challenges that are primarily related to public health. It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues. Public health requires special attention as it drives economy and education system. COVID-19 is an example—a truly infectious disease outbreak. The vision of WHO is to create public health services that can deal with abovementioned crucial challenges by focusing on the following elements: health protection, disease prevention and health promotion. For these issues, in the big data analytics era, AI and ML tools/techniques have potential to improve public health (e.g., existing healthcare solutions and wellness services). In other words, they have proved to be valuable tools not only to analyze/diagnose pathology but also to accelerate decision-making procedure especially when we consider resource-constrained regions.
650 2 4 $a Public Health. $3 592982
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Health Informatics. $3 593963
650 2 4 $a Artificial Intelligence. $3 646849
650 1 4 $a Computational Intelligence. $3 768837
650 0 $a Public health. $3 560998
650 0 $a Machine learning. $3 561253
650 0 $a Health informatics. $3 1064466
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computational intelligence. $3 568984
700 1 $a Gaur, Loveleen. $e author. $1 https://orcid.org/0000-0002-0885-1550 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1363589
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811667671
776 0 8 $i Printed edition: $z 9789811667695
830 0 $a SpringerBriefs in Computational Intelligence, $x 2625-3704 $3 1254760
856 4 0 $u https://doi.org/10.1007/978-981-16-6768-8
912 $a ZDB-2-SLS
912 $a ZDB-2-SXS
950 $a Social Sciences (SpringerNature-41176)
950 $a Social Sciences (R0) (SpringerNature-43726)