An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing A development and usability study

doi:10.1097/MD.0000000000028457

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 其他文獻 > Item 310902500/24220

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题名:	An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing A development and usability study
作者:	Hsu, CF;Chien, TW;Yan, YH
关键词:	computerized adaptive testing;convolutional neural network;my health bank;Rasch model;receiver operating characteristic curve;value cocreation
日期:	2021
上传时间:	2022-08-05T10:45:01Z (UTC)
出版者:	LIPPINCOTT WILLIAMS & WILKINS
ISSN:	0025-7974
摘要:	Background: The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT. Methods: In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions. Results: We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97-0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents' burdens using Rasch MCAT. Conclusions: The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients' perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.
URI:	http://dx.doi.org/10.1097/MD.0000000000028457 https://www.webofscience.com/wos/woscc/full-record/WOS:000736149900005 https://ir.csmu.edu.tw:8080/handle/310902500/24220
關聯:	MEDICINE ,2021 ,v100 ,issue52
显示于类别:	[中山醫學大學研究成果] 其他文獻

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