Abiodun Theresa N, Okunbor Daniel, Osamor Victor Chukwudi
Department of Computer and Information Sciences, Covenant University, Ota, Nigeria.
Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, United States.
Health Technol (Berl). 2022;12(2):359-364. doi: 10.1007/s12553-022-00652-z. Epub 2022 Mar 11.
Monitoring any process is crucial and very necessary, this is to ensure that standard protocols and procedures are strictly adhered to, monitoring clinical trials is not an exception. It is one of the most crucial processes that should be monitored because human subjects are involved. In trying to monitor clinical trial, information and communication technology techniques can be deployed to facilitate the process and hence improve accuracy. This research formulates a new conceptual framework for monitoring clinical trial using Support Vector Machine and Artificial Neural Network classifiers with physiological datasets from a wearable device. The proposed framework prototype consists of data collection module, data transmission module, and data analysis and prediction module. The data analytic and prediction module is the core section of the proposed framework tailored with data analysis. These datasets are preprocessed and transformed and then used to train and test the system, through different experimental analysis including bagging Support Vector Machine (SVM) and Artificial Neural Network (ANN). The outcome of the analysis presents classification into three different categories, such as fit, unfit, and undecided participants. These various classifications are used to determine if a participant should be allowed to continue in the trial or not. This research provides a framework that is useful in monitoring clinical trial remotely, thereby informing the decision-making process of the research team.
监测任何过程都是至关重要且非常必要的，这是为了确保严格遵守标准协议和程序，监测临床试验也不例外。它是最关键的需要监测的过程之一，因为涉及人类受试者。在尝试监测临床试验时，可以部署信息和通信技术来促进这一过程，从而提高准确性。本研究使用支持向量机和人工神经网络分类器以及来自可穿戴设备的生理数据集，为监测临床试验制定了一个新的概念框架。所提出的框架原型包括数据收集模块、数据传输模块以及数据分析与预测模块。数据分析与预测模块是所提出框架中经过数据分析定制的核心部分。这些数据集经过预处理和转换，然后通过包括装袋支持向量机（SVM）和人工神经网络（ANN）在内的不同实验分析，用于训练和测试系统。分析结果呈现出分为三种不同类别的分类，例如适合、不适合和未决定的参与者。这些不同的分类用于确定参与者是否应被允许继续试验。本研究提供了一个有助于远程监测临床试验的框架，从而为研究团队的决策过程提供信息。
