Giannopoulou Panagiota, Vrahatis Aristidis G, Papalaskari Mary-Angela, Vlamos Panagiotis
Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.
Department of Computing Science, Villanova University, Villanova, PA 19085, USA.
Healthcare (Basel). 2023 Nov 19;11(22):2985. doi: 10.3390/healthcare11222985.
Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.
神经认知障碍（NCDs）是一个重大的全球健康问题，早期检测对于优化治疗效果至关重要。与此同时，移动健康应用程序（mHealth应用程序）已成为帮助认知缺陷患者的一个有前景的途径。在此背景下，我们率先开发了RODI mHealth应用程序，这是一种通过一系列简短任务来检测符合NCDs标准的独特方法。利用RODI应用程序，我们在2022年7月至10月期间对182名患有NCDs的个体和健康参与者进行了一项研究。该研究旨在评估健康老年人和NCD患者之间的表现差异，确定在首次使用RODI应用程序时的显著表现差异，并确定预测结果的关键特征。随后，对结果进行机器学习处理，以揭示与NCDs相关的潜在模式。我们根据RODI中的任务与NCDs标准的符合程度对其进行优先级排序，从而将其作为该疾病的关键数字指标。我们通过采用一种集成策略来实现这一点，该策略利用了三种当代分类算法的特征重要性机制。我们的分析表明，与视觉工作记忆相关的任务在区分健康个体和患有NCDs的个体方面最为显著。另一方面，涉及心算、执行工作记忆和回忆的过程在检测过程中的影响较小。我们的研究为未来的mHealth应用程序提供了蓝图，为加强对疾病和相关病症的数字指标检测提供了指导。
