医联体联合病房云医疗业务系统设计与应用

为实现医联体上下级医疗机构之间医疗业务的无缝衔接,华中科技大学同济医学院附属武汉市中心医院利用互联网技术,构建了上下级医疗机构之间的信息共享与交换机制,研发了医联体联合病房云医疗业务应用系统。该系统实现了上下级医疗机构的医疗资源与数据信息共享,方便患者就医,提高医疗服务水平。     

基于穿戴式智慧衣的心电远程实时监护系统设计

目的:针对目前心电监护系统在穿戴式、实时性和数据分析上存在的不足之处,设计一种基于穿戴式智慧衣的心电远程实时监护系统。方法:该系统包括穿戴式智慧衣、Android智能手机App和私有云服务器3个部分。通过穿戴式智慧衣实时采集老年人心电信号。Android智能手机App通过无线蓝牙技术接收心电数据,实时传输心电数据到私有云服务器。私有云服务器接收智能手机传输的心电数据,采用基于机器学习的心电分类算法对心电数据进行房颤检测。云端服务器的云端心电图实时监护平台实时显示心电图和心电分析结果,辅助社区监护人员监护老年人心电。结果:心电分类算法的房颤检测效果较好,该系统的心电数据采集和数据远程传输可靠,监护功能运行正常。结论:该系统可以在社区中对老年人进行心电远程实时监护和房颤检测。     

疫情催生的“云馆配”与其他采访模式的比较与思考

以浙江省首届“云馆配”图书展示会为例，介绍了“云馆配”采访模式的具体实践，对“云馆配”与现场采访、征订目录采访、读者荐购、读者决策采购等不同采访模式进行了比较，指出了各种模式的优势与不足，提出了“云馆配”采访模式的优化发展策略。认为“云馆配”立足用户需求，不仅使图书馆（用户）、馆配商、出版社三方得到了及时、有效的沟通，还成为读者决策采购的一种新的积极尝试。“云馆配”采访模式已在疫情期间得到了较好应用，未来可辅助日常图书采访。     

A neuromorphic network for generic multivariate data classification

Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using “virtual receptors” (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a naïve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-to-trial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to real-world computing problems.The remarkable sensory and behavioral capabilities of all higher organisms are provided by the network of neurons in their nervous systems. The computing principles of the brain have inspired many powerful algorithms for data processing, most importantly the perceptron and, building on top of that, multilayer artificial neural networks, which are being applied with great success to various data analysis problems (1). Although these networks operate with continuous values, computation in biological neuronal networks relies on the exchange of action potentials, or “spikes.”Simulating networks of spiking neurons with software tools is computationally intensive, imposing limits to the duration of simulations and maximum network size. To overcome this limitation, several groups around the world have started to develop hardware realizations of spiking neuron models and neuronal networks (2–10) for studying the behavior of biological networks (11). The approach of the Spikey hardware system used in the present study is to enable high-throughput network simulations by speeding up computation by a factor of 10⁴ compared with biological real time (12, 13). It has been developed as a reconfigurable multineuron computing substrate supporting a wide range of network topologies (14).In addition to providing faster tools for neurosimulation, high-throughput spiking network computation in hardware offers the possibility of using spiking networks to solve real-world computational problems. The massive parallelism is a potential advantage over conventional computing when processing large amounts of data in parallel. However, conventional algorithms are often difficult to implement using spiking networks for which many neuromorphic hardware substrates are designed. Novel algorithms have to be designed that embrace the inherent parallelism of a brain-like computing architecture.A common problem in data analysis is classification of multivariate data. Many problems in artificial intelligence relate to classification in some way or the other, such as object recognition or decision making. It is the basis for data mining and, as such, has widespread applications in industry. We interact with classification systems in many aspects of daily life, for example in the form of Web shop recommendations, driver assistance systems, or when sending a letter with a handwritten address that is deciphered automatically in the post office.In this work, we present a neuromorphic network for supervised classification of multivariate data. We implemented the spiking network part on a neuromorphic hardware system. Using a range of datasets, we demonstrate how the classifier network supports nonlinear separation through encoding by virtual receptors, whereas lateral inhibition transforms the input data into a sparser encoding that is better suited for learning.     

太极云手训练对颈性眩晕患者康复效果的影响

目的探讨太级云手训练对颈性眩晕患者眩晕症状与活动功能的影响。方法将60例颈性眩晕患者按入院顺序分为观察组和对照组各30例。对照组给予常规治疗和护理,观察组在此基础上给予太极云手练习,均干预3周。采用多功能颈椎检测系统(MCU)和颈性眩晕症状与功能评估量表评价锻炼效果。结果观察组干预后左侧屈活动度、右侧屈活动度显著优于对照组(均P<0.01),颈性眩晕症状与功能评分显著高于对照组(P<0.05)。结论太级云手训练有助于改善颈性眩晕患者眩晕症状和提高患者的颈部活动度。     

基于自组网和物联网的医疗设备综合管理系统设计与实现

目的:针对传统医疗设备管理系统电子信息化方面的不足,设计一种新的基于自组网和物联网技术医疗设备综合管理系统,以提高医院对医疗设备的管理效率。方法:引入自组网和云计算网络技术,实现各个管理系统模块之间快速无缝链接,并提供完善的操作手册和设备状态等信息。结果:该系统采用了新型监控和无线组网技术,具有监控医院所有医疗仪器设备的运行状况,并记录和统计各种运行信息,实现对医院所有医疗仪器设备的管理、跟踪和统计等功能;此外,提供的手册可以方便地检查仪器设备的健康状态,以便于维护和维修。结论:基于自组网和物联网技术医疗设备综合管理系统,通过实际部署使用,验证了该系统的成熟性和可靠性,可以大幅提高医疗设备的使用效率。     

Evaluation of the implementation and effect of the healthcare cloud information platform for diabetes self‐management: A case study in Shanghai

With the high prevalence of diabetes, its long‐term treatment, and many complications, there is a need for a new diabetes self‐management model in developing countries. Therefore, this study aimed to assess the implementation and effect of a new diabetes self‐management model, known as the healthcare cloud information platform, in Shanghai, China. The implementation of the new model was evaluated in terms of the equipment coverage in community health service centres and the services provided. The effect was assessed according to the treatment rate and control rate from 2012 to 2017. Their correlations with the self‐management rate were analysed using Spearman's test. It was found that the Shanghai healthcare cloud information platform had covered a total of 237 community health service centres in all of the 16 administrative districts, equipping them with 2489 health stations, by 2017. An average of 8966 people uploaded data to the healthcare cloud information platform every day, with the accumulated service up to 12 043 508 person‐times and an average daily increase of 13 227.9. The treatment rate of diabetes patients in Shanghai increased from 42.9% in 2012 to 78.0% in 2017, and the control rate increased to 72% in 2017, compared with 63% in 2012. Furthermore, there was a significant correlation between the treatment rate, control rate, and self‐management rate. The findings indicate that it is feasible to implement the new healthcare cloud information platform for diabetes self‐management and that it is effective.     

基于粒计算的模糊神经建模方法在电能输出预测中的应用

准确地预测电厂的电能输出可以节约成本从而获得最大利润,因此建立一个模型来预测电厂的满载电功率输出是非常重要的。粒计算(Granular Computing, GrC)是一种新型的数据挖掘方法,它将具有类似特性的对象组合在一起,通过选择合适的粒度提取核心信息,减少冗余,降低问题求解的复杂度。本文使用GrC方法,从复杂多维数据集中以信息粒的形式建立初始的模糊推理系统,再通过模糊神经网络学习方法对系统参数进行优化。这种基于GrC的模糊神经(Granular Computing based Neuro-Fuzzy, GrC-NF)建模方法,不仅可以降低问题求解的复杂度,而且可以保持模糊逻辑系统的可解释性,将其与模糊神经网络的结合又提高了建模精度。本文将该方法用于建立电功率输出的预测模型,通过其预测精度的比较表明了该方法的优越性。