國立虎尾科技大學 |

Analytical Methods for Diagnosis and Prediction of Health Conditions.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Analytical Methods for Diagnosis and Prediction of Health Conditions./
作者:	Davis, Tyler Austin.
面頁冊數:	1 online resource (171 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
Contained By:	Dissertations Abstracts International84-09B.
標題:	Medicine. -
電子資源:	click for full text (PQDT)
ISBN:	9798377643937

Analytical Methods for Diagnosis and Prediction of Health Conditions.
Davis, Tyler Austin.

Analytical Methods for Diagnosis and Prediction of Health Conditions. - 1 online resource (171 pages)

Source: Dissertations Abstracts International, Volume: 84-09, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Includes bibliographical references

Recent years have seen a tremendous amount of growth in the performance and adoption of artificial intelligence (AI) and machine learning (ML) systems. These systems now permeate our lives, underpinning everything from web search to credit card fraud detection and photography. In principle, these advancements could also be applied to the domain of healthcare, where they could improve patient outcomes.However, despite the almost fifty years that have elapsed since the first National Institutes of Health AI in Medicine (AIM) workshop in 1973 and the ubiquity of AI systems in our daily lives, AIM has not yet lived up to its lofty promises. AIM systems have seen limited deployment due to challenges including data missingness, data heterogeneity, explainability, and generalizability across variances in patient populations. The recent increase in the availability of electronic health record information, the variety and cost-effectiveness of mobile sensors, and the capabilities of machine learning algorithms promise to help improve healthcare delivery if challenges can be overcome. Through techniques such as interpretable analysis of heterogeneous information networks and missingness-aware modeling, we demonstrate that the challenges of AI in Medicine can be overcome in order to improve healthcare access, aid physicians, and generate new insights into disease.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798377643937Subjects--Topical Terms:

644133
Medicine.
Subjects--Index Terms:

AI systemsIndex Terms--Genre/Form:

554714
Electronic books.

Analytical Methods for Diagnosis and Prediction of Health Conditions.
LDR:02724ntm a22003857 4500 001 1146234
005 20240812064348.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798377643937
035 $a (MiAaPQ)AAI30313940
035 $a AAI30313940
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Davis, Tyler Austin. $3 1471592
245 1 0 $a Analytical Methods for Diagnosis and Prediction of Health Conditions.
264 0 $c 2023
300 $a 1 online resource (171 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-09, Section: B.
500 $a Advisor: Sarrafzadeh, Majid.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2023.
504 $a Includes bibliographical references
520 $a Recent years have seen a tremendous amount of growth in the performance and adoption of artificial intelligence (AI) and machine learning (ML) systems. These systems now permeate our lives, underpinning everything from web search to credit card fraud detection and photography. In principle, these advancements could also be applied to the domain of healthcare, where they could improve patient outcomes.However, despite the almost fifty years that have elapsed since the first National Institutes of Health AI in Medicine (AIM) workshop in 1973 and the ubiquity of AI systems in our daily lives, AIM has not yet lived up to its lofty promises. AIM systems have seen limited deployment due to challenges including data missingness, data heterogeneity, explainability, and generalizability across variances in patient populations. The recent increase in the availability of electronic health record information, the variety and cost-effectiveness of mobile sensors, and the capabilities of machine learning algorithms promise to help improve healthcare delivery if challenges can be overcome. Through techniques such as interpretable analysis of heterogeneous information networks and missingness-aware modeling, we demonstrate that the challenges of AI in Medicine can be overcome in order to improve healthcare access, aid physicians, and generate new insights into disease.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Medicine. $3 644133
650 4 $a Ophthalmology. $3 644487
653 $a AI systems
653 $a Machine learning
653 $a Healthcare access
653 $a Patient outcomes
655 7 $a Electronic books. $2 local $3 554714
690 $a 0800
690 $a 0564
690 $a 0381
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Los Angeles. $b Computer Science 0201. $3 1182286
773 0 $t Dissertations Abstracts International $g 84-09B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313940 $z click for full text (PQDT)