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

切換: 標籤 | MARC模式 | ISBD

Machine learning in medicine = a com...

Cleophas, Ton J.

FindBook

Google Book

Amazon

博客來

Machine learning in medicine = a complete overview /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning in medicine/ by Ton J. Cleophas, Aeilko H. Zwinderman.
其他題名:	a complete overview /
作者:	Cleophas, Ton J.
其他作者:	Zwinderman, Aeilko H.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xxx, 667 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-33970-8
ISBN:	9783030339708

Machine learning in medicine = a complete overview /
Cleophas, Ton J.

Machine learning in medicinea complete overview /[electronic resource] :by Ton J. Cleophas, Aeilko H. Zwinderman. - Second edition. - Cham :Springer International Publishing :2020. - xxx, 667 p. :ill., digital ;24 cm.

Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them. The main purpose of the first edition was, to provide stepwise analyses of the novel methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information". Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials. Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis. Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.

ISBN: 9783030339708

Standard No.: 10.1007/978-3-030-33970-8doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: R859.7.A78 / C54 2020

Dewey Class. No.: 610.285

Machine learning in medicine = a complete overview /
LDR:02943nmm a2200325 a 4500 001 2257785
003 DE-He213
005 20200706131054.0
006 m d
007 cr nn 008maaau
008 220420s2020 sz s 0 eng d
020 $a 9783030339708 $q (electronic bk.)
020 $a 9783030339692 $q (paper)
024 7 $a 10.1007/978-3-030-33970-8 $2 doi
035 $a 978-3-030-33970-8
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.A78 $b C54 2020
072 7 $a MBGR $2 bicssc
072 7 $a MED000000 $2 bisacsh
072 7 $a MBGR $2 thema
082 0 4 $a 610.285 $2 23
090 $a R859.7.A78 $b C628 2020
100 1 $a Cleophas, Ton J. $3 1001966
245 1 0 $a Machine learning in medicine $h [electronic resource] : $b a complete overview / $c by Ton J. Cleophas, Aeilko H. Zwinderman.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xxx, 667 p. : $b ill., digital ; $c 24 cm.
520 $a Adequate health and health care is no longer possible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies. The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector, and it was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In this second edition the authors have removed the textual errors from the first edition. Also, the improved tables from the first edition, have been replaced with the original tables from the software programs as applied. This is, because, unlike the former, the latter were without error, and readers were better familiar with them. The main purpose of the first edition was, to provide stepwise analyses of the novel methods from data examples, but background information and clinical relevance information may have been somewhat lacking. Therefore, each chapter now contains a section entitled "Background Information". Machine learning may be more informative, and may provide better sensitivity of testing than traditional analytic methods may do. In the second edition a place has been given for the use of machine learning not only to the analysis of observational clinical data, but also to that of controlled clinical trials. Unlike the first edition, the second edition has drawings in full color providing a helpful extra dimension to the data analysis. Several machine learning methodologies not yet covered in the first edition, but increasingly important today, have been included in this updated edition, for example, negative binomial and Poisson regressions, sparse canonical analysis, Firth's bias adjusted logistic analysis, omics research, eigenvalues and eigenvectors.
650 0 $a Machine learning. $3 533906
650 0 $a Medical informatics. $3 661258
650 0 $a Artificial intelligence $x Medical applications. $3 900591
650 1 4 $a Biomedicine, general. $3 3446035
650 2 4 $a Medicine/Public Health, general. $3 891342
650 2 4 $a Statistics, general. $3 896933
650 2 4 $a Science, Humanities and Social Sciences, multidisciplinary. $3 3134887
700 1 $a Zwinderman, Aeilko H. $3 1243989
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-33970-8
950 $a Biomedical and Life Sciences (SpringerNature-11642)