我国移动医疗应用服务监管刍议

通过阐述移动医疗应用服务的内涵和外延，介绍了其应用领域和优势特点，重点分析了目前我国移动医疗应用服务的发展困境，如应用软件准入门槛低、医生在线执业不合法、移动医疗引发安全风险等问题。同时借鉴美国移动医疗监管模式，从移动医疗立法、主体监管部门、分类管理制度等角度展开探讨，希冀推动移动医疗的蓬勃发展，让人人享有基本医疗卫生服务。     

Health Concerns Versus Mobile Data Needs: Conjoint Measurement of Preferences for Mobile Communication Network Scenarios

As demands for mobile broadband services and ubiquitous network coverage in our societies are increasing, the mobile communication network infrastructure has to be expanded. Concurrently, the technical infrastructure of mobile communication technologies (base stations) raises the public's concerns about health risks due to electromagnetic fields (EMF). By applying conjoint analyses, the study empirically investigates the relationship between mobile data demands, different base station locations, the prevalence of perceived health complaints, and the impact of compensation payments. Findings show that health concerns are the most critical factor for mobile network communication scenario preferences, followed by data rate availability. In the decision scenarios, base station location and compensation payments played a minor role. Two user groups, cellphone and smart phone users who differ in their sensitivity regarding health concerns and data demands, were identified by segmentation analysis. By means of a sensitivity analysis, different mobile communication network scenarios were analyzed. Outcomes show the importance of integrating users’ preferences into the design of mobile communication networks. This especially refers to an increased sensitivity regarding health concerns in cellphone users and minimum requirements for data rates at least sufficient for the usage of mobile Internet services for smart phone users.     

Effect of polynomial,radial basis,and Pearson VII function kernels in support vector machine algorithm for classification of crayfish

Freshwater crayfish are one of the most important aquatic organisms that play a pivotal role in the aquatic food chain as well as serving as bioindicators for the aquatic ecosystem health assessment. Hemocytes, the blood cells of crustaceans, can be considered stress and health indicators in crayfish, and are used to evaluate the health response. Therefore, total hemocyte cell numbers (THCs) are useful parameters to show the health of crustaceans and serve as stress indicators to decide the quality of the habitat. Since, catching the fish and the other aquatic organisms, and collecting the data for further assessments are time-consuming and frustrating, today, scientists tend to use swift, more sophisticated, and more reliable methods for modeling the ecosystem stressors based on bioindicators. One tool which has attracted the attention of science communities in the last decades is machine learning algorithms that are reliable and accurate methods to solve classification and regression problems. In this study, a support vector machine is carried out as a machine learning algorithm to classify healthy and unhealthy crayfish based on physiological characteristics. To solve the non-linearity problem of the data by transporting data to high-dimensional space, different kernel functions including polynomial (PK), Pearson VII function-based universal (PUK), and radial basis function (RBF) kernels are used and their effect on the performance of the SVM model was evaluated. Both PK and PUK functions performed well in classifying the crayfish. RBF, however, had an adverse impact on the performance of the model. PUK kernel exhibited an outstanding performance (Accuracy = 100%) for the classification of the healthy and unhealthy crayfish.     

Combining classifiers for HIV-1 drug resistance prediction

This paper applies and studies the behavior of three learning algorithms, i.e. the Support Vector machine (SVM), the Radial Basis Function Network (the RBF network), and k-Nearest Neighbor (k-NN) for predicting HIV-1 drug resistance from genotype data. In addition, a new algorithm for classifier combination is proposed. The results of comparing the predictive performance of three learning algorithms show that, SVM yields the highest average accuracy, the RBF network gives the highest sensitivity, and k-NN yields the best in specificity. Finally, the comparison of the predictive performance of the composite classifier with three learning algorithms demonstrates that the proposed composite classifier provides the highest average accuracy.     

放射治疗网络系统的设计和应用

目的:研发放射治疗计划和放射治疗信息管理系统。方法:放疗网络采用客户机服务器模式,Oracle 9i为数据库服务器;使用PowerBuilder9i为编程语言进行开发。结果:该系统包括医生模块(放射治疗计划模块)、技术员模块、物理师模块、放射治疗电子病历查询和统计模块、系统管理模块五个部分。结论:该系统运行稳定,数据安全可靠,操作简单,可作为科室信息化建设的重要组成部分。     

基于机器视觉和深度学习的稻纵卷叶螟性诱智能监测系统

【目的】为减轻基层测报人员工作量,提高稻纵卷叶螟Cnaphalocrocis medinalis性诱测报的准确率和实时性,实现监测数据可追溯,建立了基于机器视觉的稻纵卷叶螟性诱智能监测系统。【方法】稻纵卷叶螟性诱智能监测系统包括基于机器视觉的智能性诱捕器、基于深度学习的稻纵卷叶螟检测模型、系统Web前端和服务器端。利用工业相机、光源和Android平板搭建了智能性诱捕器的机器视觉系统;建立了基于改进的YOLOv3和DBTNet-101双层网络的稻纵卷叶螟检测模型;利用HTML, CSS, JavaScript和Vue搭建系统Web前端展示稻纵卷叶螟检测与计数结果;使用Django框架搭建服务器端,对来自智能性诱捕器通过4G网络上传的图像进行接收与结果反馈;采用MySQL数据库保存图像和模型检测结果等信息。【结果】基于机器视觉的稻纵卷叶螟性诱智能监测系统利用智能性诱捕器自动定期上传稻纵卷叶螟图像至服务器,部署在服务器上的目标检测模型对稻纵卷叶螟成虫进行实时自动检测,精确率和召回率分别达97.6%和98.6%;用户可通过Web前端查看稻纵卷叶螟检测结果图。【结论】基于机器视觉的稻纵卷叶螟性...     

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients’ benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.     

A novel method for protein secondary structure prediction using dual-layer SVM and profiles

A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than that of traditional machine learning approaches. The performance was further improved by combining PSSM profiles with the SVM analysis. The PSSMs were generated from PSI-BLAST profiles, which contain important evolution information. The final prediction results were generated from the second SVM layer output. On the CB513 data set, the three-state overall per-residue accuracy, Q3, reached 75.2%, while segment overlap (SOV) accuracy increased to 80.0%. On the CB396 data set, the Q3 of our method reached 74.0% and the SOV reached 78.1%. A web server utilizing the method has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm.     

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data

Background

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming.

Methodology/Principal Findings

Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time.

Conclusions/Significance

The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.     

Life Cycle Assessment of the Mobile Communication System UMTS: Towards Eco-efficient Systems (12 pp)

Goal, Scope and Background Goal of this study is an evaluation of the environmental sustainability of the UMTS mobile communication system in Switzerland by means of a Life Cycle Assessment (LCA). A baseline environmental impact profile across the full life cycle of the UMTS (Universal Mobile Telecommunication System) and its predecessor, the GSM (Global System for Mobile Communication) is presented. The baseline assessment was a necessary first step to evaluate the environmental impacts of the mobile communication systems use and growth, thus permitting the evaluation of its environmental sustainability. Main Features Two functional units are defined: a data set of 1 Gbit (1.000.000 kbit), and the yearly mobile communication of an average customer. In the UMTS, both data packages and calls can be conveyed. In order to be able to standardize the results, an equivalence between these two kinds of transmission is formed. Two different options are defined, which represent different ways of transferring the data: mobile phone to mobile phone, and mobile phone to fixed network. All components of the UMTS network like the mobile phones, base stations, antennae, switching systems and the components of the landline like cable system and switching centers, are assessed. The environmental impacts are assessed taking into account all major life cycle phases like raw material extraction, manufacturing, use, disassembly and disposal of the product and the needed infrastructure. Electronic components like printed wiring boards and integrated circuits are assessed using a simple model based on the size (for IC) or number of layers (for PWB), respectively. Mining of precious metals (gold, silver) is included. The study was carried out by ESU-services, Motorola, Swisscom and Deutsche Telekom. Thanks to the industrial partners it can rely on primary data for the production of mobile phone and base station, and for the operation of the networks. As the UMTS network is still being built, no actual data of network operation is available. Data from the GSM (Global System for Mobile Communication) were used in case of data gaps. Results and Conclusions About 25 kg CO2 are emitted and 800 MJ-eq (non-renewable) primary energy are required for the transfer of 1 Gbit information from mobile phone to mobile phone in the UMTS network. For a transfer from mobile to fixed network, these values are 20 kg CO2 and 640 MJ-eq, respectively. On the other hand, the fixed network requires more resources like copper (0.07 kg for the mobile to mobile option vs. 0.12 kg for mobile to fixed network). From an environmental point of view, the mobile telephone is the most important element of the mobile communication network (UMTS and GSM). The short service life of the mobile phone plays a substantial role. Increasing the utilization period of the mobile phone (e.g. by leasing, re-use, extension of the innovation cycles, etc.) could thus represent a large potential for its improvement. The second most important components are the base stations. In the assessment mainly the use phase proved to be important. The lower environmental impact (per Gbit data transfer) as compared to the mobile phone can be explained by the longer service life (around factor 8). Main impacts are caused by the electricity consumption, in particular the energy needed for cooling the base stations. By choosing an environmentally benign electricity mix and/or by increasing the portion of renewable sources of energy, the network operators have a substantial potential of lower the environmental impacts (in particular the greenhouse gas emissions) of mobile telecommunication. Furthermore, the manufacturing of electronic components, the life time of the appliances and energy consumption are key parameters influencing the environmental profile of the networks most. Given its larger data transfer rate, the UMTS is ecologically more favorable in terms of data transfer rate than its predecessor, the GSM system. The higher energy consumption and the more complex production of the devices in the UMTS system are compensated by the faster data transmission rate. Per customer, the result is inverse, however, since the higher efficiency is compensated by the higher data communication per user in the UMTS system. The UMTS network in its state of 2004 according to the 2001 planning and with the accordingly calculated number of customers and data transfer causes 2.1 times more CO2 emissions and requires 2.4 times more (non-renewable) primary energy per customer than for the GSM system in its current state. It must be noted, however, that the UMTS technology supports other services than the GSM system. The development of the UMTS is accompanied with an increased consumption of resources and emissions of pollutants and greenhouse gases regarding the entire system for mobile telephone communication. The GSM system is a mature technology, while the UMTS is still at the beginning of its learning curve. Thus, it can be safely assumed that large improvement potentials are still present for the UMTS network components concerning expenditures and emissions both at production and by the use of the devices. This study provides the necessary information where such improvements are most effective in environmental terms.     

A Smartphone App and Cloud-Based Consultation System for Burn Injury Emergency Care

Background

Each year more than 10 million people worldwide are burned severely enough to require medical attention, with clinical outcomes noticeably worse in resource poor settings. Expert clinical advice on acute injuries can play a determinant role and there is a need for novel approaches that allow for timely access to advice. We developed an interactive mobile phone application that enables transfer of both patient data and pictures of a wound from the point-of-care to a remote burns expert who, in turn, provides advice back.

Methods and Results

The application is an integrated clinical decision support system that includes a mobile phone application and server software running in a cloud environment. The client application is installed on a smartphone and structured patient data and photographs can be captured in a protocol driven manner. The user can indicate the specific injured body surface(s) through a touchscreen interface and an integrated calculator estimates the total body surface area that the burn injury affects. Predefined standardised care advice including total fluid requirement is provided immediately by the software and the case data are relayed to a cloud server. A text message is automatically sent to a burn expert on call who then can access the cloud server with the smartphone app or a web browser, review the case and pictures, and respond with both structured and personalized advice to the health care professional at the point-of-care.

Conclusions

In this article, we present the design of the smartphone and the server application alongside the type of structured patient data collected together with the pictures taken at point-of-care. We report on how the application will be introduced at point-of-care and how its clinical impact will be evaluated prior to roll out. Challenges, strengths and limitations of the system are identified that may help materialising or hinder the expected outcome to provide a solution for remote consultation on burns that can be integrated into routine acute clinical care and thereby promote equity in injury emergency care, a growing public health burden.     

Ultrasound? Fetal monitoring? Spectrometer? There's an app for that!: biomedical smart phone apps are taking healthcare by storm

If you think your doctor is a mobile phone junkie now, you haven't seen anything yet. A profusion of new software applications, or apps, are either already here or coming soon to convert smart phones into biomedical devices that will play a larger role in healthcare. Engineers, computer programmers, medical professionals, and other researchers are jumping on the bandwagon to create apps and add-on devices, or peripherals, that turn a smart phone into a microscope, an ultrasound machine, or a heart-rate monitor, just to name a few.     

支持向量机与神经网络的关系研究

支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点,该方法已经广泛用于解决分类和回归问题．本文将结构风险函数应用于径向基函数网络学习中,同时讨论了支持向量回归模型和径向基函数网络之间的关系．仿真实例表明所给算法提高了径向基函数网络的泛化性能．     

Personal exposure to mobile phone frequencies and well-being in adults: a cross-sectional study based on dosimetry

The use of mobile phone telecommunication has increased in recent years. In parallel, there is growing concern about possible adverse health effects of cellular phone networks. We used personal dosimetry to investigate the association between exposure to mobile phone frequencies and well-being in adults. A random population-based sample of 329 adults living in four different Bavarian towns was assembled for the study. Using a dosimeter (ESM-140 Maschek Electronics), we obtained an exposure profile over 24 h for three mobile phone frequency ranges (measurement interval 1 s, limit of determination 0.05 V/m). Exposure levels over waking hours were totalled and expressed as mean percentage of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) reference level. Each participant reported acute symptoms in a day-long diary. Data on five groups of chronic symptoms and potential confounders were assessed during an interview. The overall exposure to high-frequency electromagnetic fields was markedly below the ICNIRP reference level. We did not find any statistically significant association between the exposure and chronic symptoms or between the exposure and acute symptoms. Larger studies using mobile phone dosimetry are warranted to confirm these findings.     

Mobile Phone Use,Blood Lead Levels,and Attention Deficit Hyperactivity Symptoms in Children: A Longitudinal Study

Background

Concerns have developed for the possible negative health effects of radiofrequency electromagnetic field (RF-EMF) exposure to children’s brains. The purpose of this longitudinal study was to investigate the association between mobile phone use and symptoms of Attention Deficit Hyperactivity Disorder (ADHD) considering the modifying effect of lead exposure.

Methods

A total of 2,422 children at 27 elementary schools in 10 Korean cities were examined and followed up 2 years later. Parents or guardians were administered a questionnaire including the Korean version of the ADHD rating scale and questions about mobile phone use, as well as socio-demographic factors. The ADHD symptom risk for mobile phone use was estimated at two time points using logistic regression and combined over 2 years using the generalized estimating equation model with repeatedly measured variables of mobile phone use, blood lead, and ADHD symptoms, adjusted for covariates.

Results

The ADHD symptom risk associated with mobile phone use for voice calls but the association was limited to children exposed to relatively high lead.

Conclusions

The results suggest that simultaneous exposure to lead and RF from mobile phone use was associated with increased ADHD symptom risk, although possible reverse causality could not be ruled out.     

Mobile Phones: The Next Step towards Healthcare Delivery in Rural India?

Background

Given the ubiquity of mobile phones, their use to support healthcare in the Indian context is inevitable. It is however necessary to assess end-user perceptions regarding mobile health interventions especially in the rural Indian context prior to its use in healthcare. This would contextualize the use of mobile phone communication for health to 70% of the country''s population that resides in rural India.

Objectives

To explore the acceptability of delivering healthcare interventions through mobile phones among users in a village in rural Bangalore.

Methods

This was an exploratory study of 488 mobile phone users, residing in a village, near Bangalore city, Karnataka, South India. A pretested, translated, interviewer-administered questionnaire was used to obtain data on mobile phone usage patterns and acceptability of the mobile phone, as a tool for health-related communication. The data is described using basic statistical measures.

Results

The primary use of mobile phones was to make or receive phone calls (100%). Text messaging (SMS) was used by only 70 (14%) of the respondents. Most of the respondents, 484 (99%), were willing to receive health-related information on their mobile phones and did not consider receiving such information, an intrusion into their personal life. While receiving reminders for drug adherence was acceptable to most 479 (98%) of our respondents, 424 (89%) preferred voice calls alone to other forms of communication. Nearly all were willing to use their mobile phones to communicate with health personnel in emergencies and 367 (75%) were willing to consult a doctor via the phone in an acute illness. Factors such as sex, English literacy, employment status, and presence of chronic disease affected preferences regarding mode and content of communication.

Conclusion

The mobile phone, as a tool for receiving health information and supporting healthcare through mHealth interventions was acceptable in the rural Indian context.     

Self-reported symptoms associated with exposure to electromagnetic fields: a questionnaire study

In the last years, it has been discussed frequently whether there are any harmful effects of electromagnetic fields on human health. Electromagnetic fields are generated by several natural and man-made sources. Part of the electromagnetic spectrum called Radiofrequency is used in communication systems such as mobile (cellular) phone and computer. The aim of our study was to explore different self-reported symptoms that may be associated with exposure to electromagnetic fields. This survey study was conducted, using a questionnaire, on 350 people aged +9 years in Turkey. The chi-square test was used for data analysis. Self-reported symptoms were headache, vertigo/dizziness, fatigue, forgetfulness, sleep disturbance-insomnia, tension-anxiety, joint and bone pain, lacrimation of the eyes, hearing loss and tinnitus. As a result of the survey, the study has shown that users of mobile phone and computer more often complained of headache, joint and bone pain, hearing loss, vertigo/dizziness, tension-anxiety symptoms according to time of daily usage (p?p?相似文献   

A configuration scheme for connectivity-aware mobile P2P networks for efficient mobile cloud-based video streaming services

Mobile cloud-based video streaming services cannot be provided seamlessly when network traffic increases sharply in congested areas such as colleges, universities, and downtown at specific times. This paper proposes a configuration scheme for connectivity-aware P2P networks which can reduce network traffic of cloud-based streaming servers through sharing of streaming video by utilizing information on connectivity status of mobile devices, and which can improve the quality of mobile cloud-based video streaming services by considering mobility of mobile devices and QoS which have not been considered in existing P2P schemes. Our proposed scheme reduces the amount of server traffic and the disconnection times of mobile devices significantly, compared to the non-P2P scheme and the AODV scheme. It also increases considerably the number of mobile devices to which a cloud-based streaming server can provide video streaming services simultaneously, compared to the two schemes.     

Mobile phone exposure and spatial memory

Radiofrequency (RF) emission during mobile phone use has been suggested to impair cognitive functions, that is, working memory. This study investigated the effects of a 2 1/2 h RF exposure (884 MHz) on spatial memory and learning, using a double-blind repeated measures design. The exposure was designed to mimic that experienced during a real-life mobile phone conversation. The design maximized the exposure to the left hemisphere. The average exposure was peak spatial specific absorption rate (psSAR10g) of 1.4 W/kg. The primary outcome measure was a "virtual" spatial navigation task modeled after the commonly used and validated Morris Water Maze. The distance traveled on each trial and the amount of improvement across trials (i.e., learning) were used as dependent variables. The participants were daily mobile phone users, with and without symptoms attributed to regular mobile phone use. Results revealed a main effect of RF exposure and a significant RF exposure by group effect on distance traveled during the trials. The symptomatic group improved their performance during RF exposure while there was no such effect in the non-symptomatic group. Until this new finding is further investigated, we can only speculate about the cause.     

Prediction of transmembrane regions of beta-barrel proteins using ANN- and SVM-based methods

This article describes a method developed for predicting transmembrane beta-barrel regions in membrane proteins using machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). The ANN used in this study is a feed-forward neural network with a standard back-propagation training algorithm. The accuracy of the ANN-based method improved significantly, from 70.4% to 80.5%, when evolutionary information was added to a single sequence as a multiple sequence alignment obtained from PSI-BLAST. We have also developed an SVM-based method using a primary sequence as input and achieved an accuracy of 77.4%. The SVM model was modified by adding 36 physicochemical parameters to the amino acid sequence information. Finally, ANN- and SVM-based methods were combined to utilize the full potential of both techniques. The accuracy and Matthews correlation coefficient (MCC) value of SVM, ANN, and combined method are 78.5%, 80.5%, and 81.8%, and 0.55, 0.63, and 0.64, respectively. These methods were trained and tested on a nonredundant data set of 16 proteins, and performance was evaluated using "leave one out cross-validation" (LOOCV). Based on this study, we have developed a Web server, TBBPred, for predicting transmembrane beta-barrel regions in proteins (available at http://www.imtech.res.in/raghava/tbbpred).