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

FindBook

Google Book

Amazon

博客來

Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence and machine learning in health care and medical sciences/ edited by Gyorgy J. Simon, Constantin Aliferis.
其他題名:	best practices and pitfalls /
其他作者:	Simon, Gyorgy J.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xxvi, 810 p. :ill. (chiefly col.), digital ;24 cm.
內容註:	Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Medical applications. -
電子資源:	https://doi.org/10.1007/978-3-031-39355-6
ISBN:	9783031393556

Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /
Artificial intelligence and machine learning in health care and medical sciencesbest practices and pitfalls /[electronic resource] :edited by Gyorgy J. Simon, Constantin Aliferis. - Cham :Springer International Publishing :2024. - xxvi, 810 p. :ill. (chiefly col.), digital ;24 cm. - Health informatics,2197-3741. - Health informatics..

Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.

Open access.

This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.

ISBN: 9783031393556

Standard No.: 10.1007/978-3-031-39355-6doiSubjects--Topical Terms:

900591
Artificial intelligence
--Medical applications.

LC Class. No.: R859.7.A78

Dewey Class. No.: 610.285

Artificial intelligence and machine learning in health care and medical sciences = best practices and pitfalls /
LDR:03264nmm a22003615a 4500 001 2370671
003 DE-He213
005 20240321192339.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031393556 $q (electronic bk.)
020 $a 9783031393549 $q (paper)
024 7 $a 10.1007/978-3-031-39355-6 $2 doi
035 $a 978-3-031-39355-6
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.A78
072 7 $a MBG $2 bicssc
072 7 $a UB $2 bicssc
072 7 $a MED117000 $2 bisacsh
072 7 $a UXT $2 thema
082 0 4 $a 610.285 $2 23
090 $a R859.7.A78 $b A791 2024
245 0 0 $a Artificial intelligence and machine learning in health care and medical sciences $h [electronic resource] : $b best practices and pitfalls / $c edited by Gyorgy J. Simon, Constantin Aliferis.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xxvi, 810 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Health informatics, $x 2197-3741
505 0 $a Predictive Analytics -- Machine Learning -- Artificial Intelligence -- Data Mining -- Clinical Risk Models -- Clinical Risk Stratification -- Data Science -- Causal Discovery -- Causal Inference -- Causal Discovery in Health Sciences -- Causal Inference In Health Sciences -- Ehr Data Analytics -- Medical Knowledge Discovery -- Biomedical Machine Learning -- Biomedical Artificial Intelligence -- Healthcare Machine Learning -- Healthcare Artificial Intelligence -- Translational Science Machine Learning -- Machine Learning for Biological Discovery -- Machine Learning in Bioinformatics -- Machine Learning in Genomics.
506 $a Open access.
520 $a This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls is a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.
650 0 $a Artificial intelligence $x Medical applications. $3 900591
650 1 4 $a Health Informatics. $3 892928
650 2 4 $a Health Care. $3 3538867
650 2 4 $a Bioinformatics. $3 553671
650 2 4 $a Public Health. $3 624351
700 1 $a Simon, Gyorgy J. $3 1286730
700 1 $a Aliferis, Constantin F. $3 3716189
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Health informatics. $3 815877
856 4 0 $u https://doi.org/10.1007/978-3-031-39355-6
950 $a Medicine (SpringerNature-11650)