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Smart Health Technologies Towards Parkinson's Disease Management.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Smart Health Technologies Towards Parkinson's Disease Management./
Author:	Zhang, Hanbin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	170 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
Subject:	Computer science. -
Online resource:	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.
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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.
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