数字化医院的网络安全问题及对策探究

数字化管理和一流的医疗设备是医院现代化的两个主要组成部分,通过计算机网络,PACS系统实现了各类医疗图像信息的集中管理和共享,HIS系统则把医院的事务管理和医疗业务进行信息化管理,计算机网络和数字化管理在医院中的作用显得日益重要。为加强网络管理,保障数字化、信息化管理的安全,本文在分析医院信息网络安全所面临问题的基础上,探讨了解决计算机病毒、信息系统崩溃、数据遗失等问题的措施。     

把脉医疗信息化

在经济全球化、社会信息化的进程中,我国医院已进入了数字化和信息化时代,大型的数字化医疗设备在医院中使用,各种医院管理信息系统和医疗临床信息系统正在普及。     

瑞金医院：统一门户激活数据生命力

如何能够整合医院各个业务系统,让现有的信息、应用和业务系统发挥其最大作用,是管理数字化医院的重大挑战之一。瑞金医院打造了统一的门户平台,激活了医院数据的“生命力”。     

数字化档案管理信息系统在医院档案管理中的应用

文章深入研究数字化档案管理信息系统在医院档案管理中的创新应用。首先,通过对医院档案管理现状的深刻分析,揭示信息时代下医院面临手动操作低效、信息碎片化等问题;其次,从科技优化的角度,探讨数字化档案管理信息系统如何通过智能算法实现档案管理的自动化处理,提高医务人员的工作效率。在人员优化方面,强调培训计划和团队协作的关键性,以推动医务人员更深入、更广泛地参与系统的应用;最后,在组织优化层面提出将系统定制化融入医院生态的策略,实现数字化档案管理信息系统与其它医疗管理信息系统的高效协同作业。通过这些深入研究,文章旨在为医院档案管理的数字化转型提供科学且实用的研究思路,以适应当今信息化医疗环境的迫切需求。     

浅析诊疗卡在应用中的问题和对扩展服务的连接

诊疗卡做为一种媒介被大部分医院作为数字化通信平台,它拓展医院数字化渠道,打通了医院信息管理系统、诊疗信息系统、办公自动化系统、文献检索服务系统、多媒体导医系统、诊疗系统、药物咨询系统等多种应用系统,贯穿了以消费信息、医疗信息、公共信息及科研资源信息为主线的全过程、全方位的现代医院数字化网络综合体系。一网多用,使院内数据资源的高度共享,使医院的管理、诊疗、科研及后勤保障工作变成一个有机的整体。     

数字化医院的现状与建设

本文论述了数字化医院发展的现状，对未来数字化医院的发展提出建议。数字化医院包括电子病历系统（EPR）、医院管理系统（HIS）、医学影像存储和传输管理系统（PACS）和检验信息系统（LIS）。它最终安现医院信息一体化，以及医疗数据的安全、有效、可靠。     

把脉医疗信息化

在经济全球化、社会信息化的进程中,我国医院已进入了数字化和信息化时代,大型的数字化医疗设备在医院中使用,各种医院管理信息系统和医疗临床信息系统正在普及。医院信息化使医院工作流程发生了改变和创新,并使医院得到了全面发展。     

手术观摩、教学系统设计方案

数字化医院的建设是以医院的业务需求为导向，以计算机网络为传输信道，以医院信息管理系统（HIS）、医疗图片处理系统（PAM）等专用软件为工作平台而不断发展和完善的过程。其中，异地手术观摩、网络教学、学术交流和手术录像的数字化归档都是近年来各大医院争相研究和发展的方向，是当今数字化医院建设中最有价值的要素。[编者按]     

高可靠 可扩展 东华助力海安县人民医院

用户概况： 海安县人民医院滨，临黄海，地处南通、泰州、盐城三市交界，始建于1950年，现已发展成为技术力量雄厚、设备先进、功能齐全、管理一流、花因式的三级综合性医院、爱婴医院、南通大学附属医院。随着海安县人民医院的医疗业务迅速发展和业务质量的不断提高，网络方面的应用越来越多，医院服务器借助网络提供了HIS系统、PACS系统这两个医院核心系统的应用，为访问者提供了越来越丰富的内容和信息。     

基于Struts框架的数字化医院安全机制研究和实现

为了推进医院医疗数字化建设,解决随之产生的信息安全问题,对数字化医院安全机制的必要性进行了分析,充分利用Struts框架的优势,概要地介绍了基于此框架的数字化医院系统及其应用模块,详细地阐述了如何通过安全机制来实现整个系统的稳定运作.引入安全机制,使得数字化医院的信息资源能够安全地传输、存储与共享,保证了系统的安全性.     

基于粗糙集的医疗数据挖掘研究与应用

医疗数据挖掘能够对现有病历数据库中数据进行自动分析并且提供有价值的医学知识。针对临床病历数据库中存在大量重复样本和冗余属性,从而影响医疗诊断的精度和速度这一问题,建立了基于信息论的粗糙集理论模型和SQL语言之间的关系,提出了基于SQL语言的条件信息熵属性约简算法,利用数据库查询语言实现了数据清洗、求核和属性约简等过程。实验结果表明该算法实现简单,运行效率高,为粗糙集理论更广泛地应用于具体的医疗数据挖掘提供了一种方法。     

医学数据网格及其相关中间件的研究与实现

研究医学数据网格，以满足共享分布式异构医学图像等医学数据资源、医学仪器等医疗资源的需要，设计了医学数据网格的体系结构，研究了医学数据标准化，设计和实现了医学应用数据库，探究了资源管理、注册查找、工作流管理，以及数据管理和安全问题。实践证明，该网格研究系统GADITMER可以有效地促进临床辅助诊断和流行病学研究的发展。     

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things (IoMT) is an online device that senses and transmits medical data from users to physicians within a time interval. In, recent years, IoMT has rapidly grown in the medical field to provide healthcare services without physical appearance. With the use of sensors, IoMT applications are used in healthcare management. In such applications, one of the most important factors is data security, given that its transmission over the network may cause obtrusion. For data security in IoMT systems, blockchain is used due to its numerous blocks for secure data storage. In this study, Blockchain-assisted secure data management framework (BSDMF) and Proof of Activity (PoA) protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system. The main aim is to enhance the data security over the networks. The PoA protocol enhances high security of data from the literature review. By replacing the malicious node from the block, the PoA can provide high security for medical data in the blockchain. Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio, precision ratio, security, and efficiency and less response time for Blockchain-enabled healthcare systems.     

DTU-PU：针对不确定数据PU学习的决策树

不确定数据的PU学习在现实世界的许多应用中,如在传感器网络、市场分析和医学诊断等领域普遍存在,提出了针对不确定数据PU学习的决策树算法。基于POSC45中信息增益的计算方法,引入UDT中处理连续属性的不确定数据时用到的不确定数据区间及概率分布函数的概念,提出了一种能处理连续属性的不确定数据PU学习的决策树算法DTU-PU（Decision Tree for Uncertain data with PU-learning）。在UCI数据集上的实验表明,DTU-PU具有较好的分类准确率和健壮性。     

用于实现医学图像ROI对比度增强的可逆数据隐藏算法

基于可逆数据隐藏技术具有可以准确地提取嵌入的秘密消息并无损还原原始载体的特点,可逆数据隐藏技术被广泛使用在医学和军事等领域。提出一种新的基于直方图平移的可逆数据隐藏算法,针对医学图像的像素分布特点,利用最大类间方差法分离背景和前景区域,确定边缘像素。对已有算法的预处理过程进行改进,使其能够自适应地选择最优预处理区间,降低视觉失真,并在信息嵌入的同时使医学图像感兴趣区域的对比度增强效果得到逐步改善,最终原始医学图像可以无失真地恢复。与现在已知的方法相比,新算法可以使医学图像ROI的对比度增强效果得到更明显的改善,大幅度提高了图像的视觉质量。     

Medical wireless IoT system and nursing intervention of chronic bronchitis based on clinical data

According to the WHO (World Health Organization), the chronic obstructive pulmonary disease has become the third leading cause of death in the group. The associated health sector related to 2030 based on the curve and the long-term cost of medical expenses. Age is the leading cause of a significant occurrence. The management of stable COPD patients to mitigate the attack, it is based on his system. Chronic obstructive pulmonary disease management system, portable spirometer and things mobile applications handheld, is a clinical information database. The Internet has established the configuration. The database, one of the MBOX format, through the Zhongshan hospital, collects data from the branch of the largest medical service provider in the region, located in the stable state of the 11 pieces of data from the main hospital of the patient follow-up. Lung function data is tested via a handheld portable spirometer and can be uploaded in real time. Other smartphone applications have been developed to establish direct communication between patients and physicians to improve disease management. It is worth noting that it classifies patient clinical data, sends it, stores it anonymously, and encrypts it for protection and privacy. What is useful in treating chronic diseases based on remote monitoring is that it significantly provides health information such as medical and biosensor data volume management and big data technology, this vast new medical technology. It can be increased. The application that needs to be processed. A potential solution for new services based on the Internet of Things is used to create the concept of big data processing.     

A hospital integrated framework for multimodality image basemanagement

The trend in healthcare information technology is increasingly digital and multimedia oriented. The next generation of health care information systems will consist of a vast network of heterogeneous, autonomous, and distributed imaging scanners, databases, information systems, knowledge intensive applications, and large quantities of multimedia medical data. A key challenge facing system researchers and builders is to provide a new organizational framework that can integrate this varied collection of resources into what appears to be a uniform and logical conglomeration of data and knowledge store in order to increase the availability of global or previously nonaccessible information and to address demanding new information processing requirements for diverse image-assisted medical applications. The purpose of this paper is to present the authors' research toward the development of a hospital integrated framework of multimodality image base management (MIBM) for digital radiology of the future. This evolutionary framework consists of three hierarchical components: a hospital-integrated picture archiving and communication system (HI-PACS), a medical image database system (MIDS), and a set of image-based medical applications that relies on the support of MIDS and PACS. In this paper, the authors describe the system architecture, guiding principles, and design specifications of HI-PACS and MIDS and illustrate their functions and capabilities with three implemented applications, namely, patient folder workflow, distributed object management, and multimodality imaging studies. In addition, the authors conclude their findings with a summary of challenges and research directions     

面向血液循环系统仿真的全身血管重建

血管的点云数据表示在医疗中有着重要的作用,可以帮助医护人员直观地了解血管的几何信息.对于医疗辅助技术、放射学模拟等应用中,除了对局部的血管系统几何信息进行可视之外,也需要对全身血管进行准确的仿真建模,从而对整体血管系统的拓扑及几何信息都需要进行系统性的重建.传统骨架提取技术无法保证血管模型正确的连接拓扑,提出了一种基于点云数据的交互式全身血管模型重建方法,通过对血管数据分段和拟合,并分析各段血管端点连接情况,重建了一种带有血管截面半径信息的全身血管一维骨架模型,且该模型能直接用于血流血压等仿真.与已有方法相比,其优势在于能应用于大规模的复杂的全身血管点云数据,且重建的模型可以直接用于仿真.BodyParts3D数据集下实验结果表明,与已有方法相比,文中方法重建的全身血管一维骨架模型具有准确的拓扑连接,能够生成更好的仿真效果.     

Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

The European 1ST DataGrid project was a pioneer in identifying the medical imaging field as an application domain that can benefit from Grid technologies. This paper describes how and for which purposes medical imaging applications can be Grid-enabled. Applications that have been deployed on the DataGrid testbed and middleware are described. They relate to medical image manipulation, including image production, secured image storage, and image processing. Results show that Grid technologies are still in their youth to address all issues related to complex medical imaging applications. If the benefit of Grid enabling for some medical applications is clear, there remain opened research and technical issues to develop and integrate all necessary services.     

Incremental rare pattern based approach for identifying outliers in medical data

Outlier detection is an imperative field of data mining that has several applications in the field of medical research. Mining outliers based on the notion of rare patterns can be a promising solution for medical diagnosis as it attempts to identify the unconventional and abnormal risk patterns present in medical data. A crucial issue in medical data analysis is the continuous growth of medical databases due to the addition of new records. Existing outlier detection techniques are capable of handling only static data and thus re-execute from scratch to identify the outliers from incremental medical data. This paper introduces an efficient rare pattern based outlier detection (RPOD) method that identifies outliers by mining rare patterns from incremental data. To avoid multiple database scans and expensive candidate generation steps performed by existent rare pattern mining techniques and facilitate incremental mining, a single pass prefix tree-based rare pattern mining technique is proposed. The proposed rare pattern mining technique is a modification of the well-known FP-Growth frequent pattern mining algorithm. Furthermore, to identify the outliers based on the set of generated rare patterns, an outlier detection technique is also presented. The significance of proposed RPOD approach is demonstrated using several well-known medical datasets. Comparative performance evaluation substantiates the predominance of RPOD approach over existing outlier mining methods.