東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Professional Computing." ]

切換: 標籤 | MARC模式 | ISBD

Machine learning and AI for healthca...

Panesar, Arjun.

FindBook

Google Book

Amazon

博客來

Machine learning and AI for healthcare = big data for improved health outcomes /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and AI for healthcare/ by Arjun Panesar.
其他題名:	big data for improved health outcomes /
作者:	Panesar, Arjun.
出版者:	Berkeley, CA :Apress : : 2021.,
面頁冊數:	xxx, 407 p. :ill., digital ;24 cm.
內容註:	Chapter 1: What Is Artificial Intelligence? -- Chapter 2: Data -- Chapter 3: What Is Machine Learning -- Chapter 4: Machine Learning Algorithms -- Chapter 5: How to Perform Machine Learning -- Chapter 6: Preparing Data -- Chapter 7: Evaluating Machine Learning Models -- Chapter 8: Machine Learning and AI Ethics -- Chapter 9: The Future of Healthcare -- Chapter 10: Case Studies -- Appendix A: References -- Appendix B: Glossary.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Medical applications. -
電子資源:	https://doi.org/10.1007/978-1-4842-6537-6
ISBN:	9781484265376

Machine learning and AI for healthcare = big data for improved health outcomes /
Panesar, Arjun.

Machine learning and AI for healthcarebig data for improved health outcomes /[electronic resource] :by Arjun Panesar. - Second edition. - Berkeley, CA :Apress :2021. - xxx, 407 p. :ill., digital ;24 cm.

Chapter 1: What Is Artificial Intelligence? -- Chapter 2: Data -- Chapter 3: What Is Machine Learning -- Chapter 4: Machine Learning Algorithms -- Chapter 5: How to Perform Machine Learning -- Chapter 6: Preparing Data -- Chapter 7: Evaluating Machine Learning Models -- Chapter 8: Machine Learning and AI Ethics -- Chapter 9: The Future of Healthcare -- Chapter 10: Case Studies -- Appendix A: References -- Appendix B: Glossary.

This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things. You will understand how machine learning can be used to develop health intelligence-with the aim of improving patient health, population health, and facilitating significant care-payer cost savings. You will: Understand key machine learning algorithms and their use and implementation within healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Manage the complexities of massive data Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents.

ISBN: 9781484265376

Standard No.: 10.1007/978-1-4842-6537-6doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78 / P36 2021

Dewey Class. No.: 610.28563

Machine learning and AI for healthcare = big data for improved health outcomes /
LDR:03171nmm a2200337 a 4500 001 2237133
003 DE-He213
005 20201215141857.0
006 m d
007 cr nn 008maaau
008 211111s2021 cau s 0 eng d
020 $a 9781484265376 $q (electronic bk.)
020 $a 9781484265369 $q (paper)
024 7 $a 10.1007/978-1-4842-6537-6 $2 doi
035 $a 978-1-4842-6537-6
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.A78 $b P36 2021
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 610.28563 $2 23
090 $a R859.7.A78 $b P191 2021
100 1 $a Panesar, Arjun. $3 3384951
245 1 0 $a Machine learning and AI for healthcare $h [electronic resource] : $b big data for improved health outcomes / $c by Arjun Panesar.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2021.
300 $a xxx, 407 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: What Is Artificial Intelligence? -- Chapter 2: Data -- Chapter 3: What Is Machine Learning -- Chapter 4: Machine Learning Algorithms -- Chapter 5: How to Perform Machine Learning -- Chapter 6: Preparing Data -- Chapter 7: Evaluating Machine Learning Models -- Chapter 8: Machine Learning and AI Ethics -- Chapter 9: The Future of Healthcare -- Chapter 10: Case Studies -- Appendix A: References -- Appendix B: Glossary.
520 $a This updated second edition offers a guided tour of machine learning algorithms and architecture design. It provides real-world applications of intelligent systems in healthcare and covers the challenges of managing big data. The book has been updated with the latest research in massive data, machine learning, and AI ethics. It covers new topics in managing the complexities of massive data, and provides examples of complex machine learning models. Updated case studies from global healthcare providers showcase the use of big data and AI in the fight against chronic and novel diseases, including COVID-19. The ethical implications of digital healthcare, analytics, and the future of AI in population health management are explored. You will learn how to create a machine learning model, evaluate its performance, and operationalize its outcomes within your organization. Case studies from leading healthcare providers cover scaling global digital services. Techniques are presented to evaluate the efficacy, suitability, and efficiency of AI machine learning applications through case studies and best practice, including the Internet of Things. You will understand how machine learning can be used to develop health intelligence-with the aim of improving patient health, population health, and facilitating significant care-payer cost savings. You will: Understand key machine learning algorithms and their use and implementation within healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Manage the complexities of massive data Be familiar with AI and healthcare best practices, feedback loops, and intelligent agents.
650 0 $a Artificial intelligence $x Medical applications. $3 900591
650 0 $a Machine learning. $3 533906
650 2 4 $a Professional Computing. $3 3201325
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6537-6
950 $a Professional and Applied Computing (SpringerNature-12059)