Department of Pediatrics, Chi Mei Medical Center, Tainan, Taiwan.
School of Medicine, College of Medicine, Chung Shan Medical University, Taichung, Taiwan.
Medicine (Baltimore). 2021 Dec 30;100(52):e28457. doi: 10.1097/MD.0000000000028457.
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.
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.
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.
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.
使用尽可能少的问题将受访者的在线意见分类为积极和消极意见，这种方法逐渐发生变化，并有助于将技术转化为实践。本研究采用包含卷积神经网络（CNN）的网络计算机化自适应测试（CAT）收集台湾用户对 My Health Bank（MHB）的认知，将其设计成一个在线模块，利用 CNN 和网络 CAT 准确、高效地将受访者的认知分为积极和消极两类。
2019 年共邀请了 640 名年龄在 20 至 70 岁之间的 MHB 注册用户完成一个包含 3 个领域、26 个项目、5 个类别的问卷，以了解他们对 MHB 的认知（PMHB26）。利用 CNN 算法和 K 均值聚类将受访者分为满意和不满意两类，并建立 PMHB26 预测模型来估计参数。同时还使用探索性因子分析、Rasch 模型和描述性统计来检验适合 CNN 和 Rasch 多维 CAT（MCAT）的人口统计学特征和 PMHB26 因子。最后设计了一个应用程序来分类 MHB 认知。
从 PMHB26 中提取了 3 个结构因子。PMHB26 各子量表的信度均大于 0.94，数据内部一致性和稳定性良好。进一步发现，PMHB26 与 CNN 结合的准确率更高（0.98），基于 391 份回复问卷，曲线下面积为 0.98（95%置信区间，0.97-0.99）；而在效率方面，使用 Rasch MCAT 可减少大约三分之一不必要的回答，从而减轻受访者的负担。
PMHB26 CNN 模型与 Rasch 在线 MCAT 相结合，可提高分类患者对 MHB 使用认知的准确性和效率。未来可将开发的应用程序用于帮助受访者自我评估 MHB 的价值共创，也可应用于其他调查。
