Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Machine Learning and AI for Healthca...
~
SpringerLink (Online service)
Machine Learning and AI for Healthcare = Big Data for Improved Health Outcomes /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Machine Learning and AI for Healthcare/ by Arjun Panesar.
Reminder of title:
Big Data for Improved Health Outcomes /
Author:
Panesar, Arjun.
Description:
XXX, 407 p. 61 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Machine learning. -
Online resource:
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 Healthcare
Big Data for Improved Health Outcomes /[electronic resource] :by Arjun Panesar. - 2nd ed. 2021. - XXX, 407 p. 61 illus.online resource.
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:
561253
Machine learning.
LC Class. No.: Q325.5-.7
Dewey Class. No.: 006.31
Machine Learning and AI for Healthcare = Big Data for Improved Health Outcomes /
LDR
:03499nam a22004095i 4500
001
1050739
003
DE-He213
005
20210913133231.0
007
cr nn 008mamaa
008
220103s2021 xxu| s |||| 0|eng d
020
$a
9781484265376
$9
978-1-4842-6537-6
024
7
$a
10.1007/978-1-4842-6537-6
$2
doi
035
$a
978-1-4842-6537-6
050
4
$a
Q325.5-.7
050
4
$a
TK7882.P3
072
7
$a
UYQM
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQM
$2
thema
082
0 4
$a
006.31
$2
23
100
1
$a
Panesar, Arjun.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1305153
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
2nd ed. 2021.
264
1
$a
Berkeley, CA :
$b
Apress :
$b
Imprint: Apress,
$c
2021.
300
$a
XXX, 407 p. 61 illus.
$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
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
Machine learning.
$3
561253
650
0
$a
Computer software.
$3
528062
650
1 4
$a
Machine Learning.
$3
1137723
650
2 4
$a
Professional Computing.
$3
1115983
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9781484265369
776
0 8
$i
Printed edition:
$z
9781484265383
776
0 8
$i
Printed edition:
$z
9781484277287
856
4 0
$u
https://doi.org/10.1007/978-1-4842-6537-6
912
$a
ZDB-2-CWD
912
$a
ZDB-2-SXPC
950
$a
Professional and Applied Computing (SpringerNature-12059)
950
$a
Professional and Applied Computing (R0) (SpringerNature-43716)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login