Edge computing is a fast-growing and much needed technology in healthcare. The problem of implementing artificial intelligence on edge devices is the complexity and high resource intensity of the most known neural network data analysis methods and algorithms. The difficulty of implementing these methods on low-power microcontrollers with small memory size calls for the development of new effective algorithms for neural networks. This study presents a new method for analyzing medical data based on the LogNNet neural network, which uses chaotic mappings to transform input information. The method effectively solves classification problems and calculates risk factors for the presence of a disease in a patient according to a set of medical health indicators. The efficiency of LogNNet in assessing perinatal risk is illustrated on cardiotocogram data obtained from the UC Irvine machine learning repository. The classification accuracy reaches ~91% with the~3–10 kB of RAM used on the Arduino microcontroller. Using the LogNNet network trained on a publicly available database of the Israeli Ministry of Health, a service concept for COVID-19 express testing is provided. A classification accuracy of ~95% is achieved, and~0.6 kB of RAM is used. In all examples, the model is tested using standard classification quality metrics: precision, recall, and F1-measure. The LogNNet architecture allows the implementation of artificial intelligence on medical peripherals of the Internet of Things with low RAM resources and can be used in clinical decision support systems.

中文翻译：

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使用 LogNNet 进行医疗数据分析的方法，用于医疗保健中的临床决策支持系统和边缘计算


边缘计算是医疗保健领域一项快速发展且急需的技术。在边缘设备上实现人工智能的问题是最知名的神经网络数据分析方法和算法的复杂性和高资源强度。在内存较小的低功耗微控制器上实现这些方法的困难需要开发新的有效神经网络算法。本研究提出了一种基于 LogNNet 神经网络的医疗数据分析新方法，该方法使用混沌映射来转换输入信息。该方法有效解决了分类问题，根据一组医疗健康指标计算出患者存在疾病的危险因素。从加州大学欧文分校机器学习存储库获得的胎心监护图数据说明了 LogNNet 在评估围产期风险方面的效率。 Arduino 微控制器上使用约 3–10 kB RAM，分类精度达到约 91%。使用在以色列卫生部公开数据库上训练的 LogNNet 网络，提供了用于 COVID-19 快速检测的服务概念。分类精度达到约 95%，并使用约 0.6 kB 的 RAM。在所有示例中，模型均使用标准分类质量指标进行测试：精度、召回率和 F1 测量。 LogNNet架构允许在低RAM资源的物联网医疗外围设备上实现人工智能，并可用于临床决策支持系统。