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

Smart Health Technologies Towards Parkinson's Disease Management.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Smart Health Technologies Towards Parkinson's Disease Management./
作者:	Zhang, Hanbin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	170 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28867024
ISBN:	9798790655364

Smart Health Technologies Towards Parkinson's Disease Management.
Zhang, Hanbin.

Smart Health Technologies Towards Parkinson's Disease Management. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 170 p.

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2022.

This item must not be sold to any third party vendors.

Parkinson's disease (PD) is a chronic and progressive movement disorder involving malfunction and death of vital nerve cells in the brain with unknown causes. About one million Americans live with PD, and approximately 60,000 Americans are diagnosed with PD each year. The combined direct and indirect cost of PD is estimated to be about $25 billion per year in the United States. Therefore, PD management, including early diagnosis, progression monitoring, and medication response, is critical for optimal treatment and intervention. At the same time, it requires episodic and costly physicians visits and is extremely challenging in practice.In the past few years, we collaborate with medical professionals to collect and integrate PD's core information through voice, gait, and electronic demographic and PD symptom-related surveys in a large-scale population. The research outcome shows potential to enhance PD diagnosis and personalized treatment for the ultimate goal of optimized (maybe optimal) PD self-management and care. Mainly, this thesis contributes to the PD self-management research in the following aspects. (1) PD Survey: we carefully survey the existing research regarding PD management using smart health technologies and summarize the challenges and opportunities. (2) PD Detection: we collaborate with healthcare professionals and collect data from people with PD using smartphones. We identify essential features for PD detection through collected mobile data and adopt deep learning-based approaches to achieve PD detection. (3) PD Medication Response: we leverage the fact that motor symptoms respond to medication quickly and design a smartphone-based passive sensing system to monitor the gait pattern at the prescribed medication time to predict if a user adheres to medication. On this basis, we synthesize a severity score and link it with activity features through a deep learning-based approach to measure the medication effectiveness.

ISBN: 9798790655364Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Deep learning

Smart Health Technologies Towards Parkinson's Disease Management.
LDR:03184nmm a2200397 4500 001 2350674
005 20221020130414.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798790655364
035 $a (MiAaPQ)AAI28867024
035 $a AAI28867024
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhang, Hanbin. $0 (orcid)0000-0002-8893-4208 $3 3690180
245 1 0 $a Smart Health Technologies Towards Parkinson's Disease Management.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 170 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
500 $a Advisor: Xu, Wenyao.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Parkinson's disease (PD) is a chronic and progressive movement disorder involving malfunction and death of vital nerve cells in the brain with unknown causes. About one million Americans live with PD, and approximately 60,000 Americans are diagnosed with PD each year. The combined direct and indirect cost of PD is estimated to be about $25 billion per year in the United States. Therefore, PD management, including early diagnosis, progression monitoring, and medication response, is critical for optimal treatment and intervention. At the same time, it requires episodic and costly physicians visits and is extremely challenging in practice.In the past few years, we collaborate with medical professionals to collect and integrate PD's core information through voice, gait, and electronic demographic and PD symptom-related surveys in a large-scale population. The research outcome shows potential to enhance PD diagnosis and personalized treatment for the ultimate goal of optimized (maybe optimal) PD self-management and care. Mainly, this thesis contributes to the PD self-management research in the following aspects. (1) PD Survey: we carefully survey the existing research regarding PD management using smart health technologies and summarize the challenges and opportunities. (2) PD Detection: we collaborate with healthcare professionals and collect data from people with PD using smartphones. We identify essential features for PD detection through collected mobile data and adopt deep learning-based approaches to achieve PD detection. (3) PD Medication Response: we leverage the fact that motor symptoms respond to medication quickly and design a smartphone-based passive sensing system to monitor the gait pattern at the prescribed medication time to predict if a user adheres to medication. On this basis, we synthesize a severity score and link it with activity features through a deep learning-based approach to measure the medication effectiveness.
590 $a School code: 0656.
650 4 $a Computer science. $3 523869
650 4 $a Health sciences. $3 3168359
650 4 $a Information technology. $3 532993
650 4 $a Medical personnel. $2 itrt. $3 774747
653 $a Deep learning
653 $a Mobile computing
653 $a Parkinson's disease
653 $a Smart health
653 $a Smartphones
653 $a Medication effectiveness
690 $a 0984
690 $a 0566
690 $a 0489
690 $a 0207
710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1035503
773 0 $t Dissertations Abstracts International $g 83-09B.
790 $a 0656
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28867024