學術產出-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

題名 A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary
作者 陸行
Luh, Hsing
Wan, Thomas T H;Matthews, Sarah;Zeng, Yong;Wang, Zhibo;Yang, Lin
貢獻者 應數系
關鍵詞 multi-criteria optimization; predictive analytics; discipline-free statistical methods; simulation modeling; multi-wave data analysis; time effect; diabetes care outcomes
日期 2022-03
上傳時間 2022-10-07
摘要 There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.
關聯 Health Services Research and Managerial Epidemiology
資料類型 article
DOI https://doi.org/10.1177/23333928221089125
dc.contributor 應數系
dc.creator (作者) 陸行
dc.creator (作者) Luh, Hsing
dc.creator (作者) Wan, Thomas T H;Matthews, Sarah;Zeng, Yong;Wang, Zhibo;Yang, Lin
dc.date (日期) 2022-03
dc.date.accessioned 2022-10-07-
dc.date.available 2022-10-07-
dc.date.issued (上傳時間) 2022-10-07-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/142232-
dc.description.abstract (摘要) There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Health Services Research and Managerial Epidemiology
dc.subject (關鍵詞) multi-criteria optimization; predictive analytics; discipline-free statistical methods; simulation modeling; multi-wave data analysis; time effect; diabetes care outcomes
dc.title (題名) A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1177/23333928221089125
dc.doi.uri (DOI) https://doi.org/10.1177/23333928221089125