边缘计算环境下基于区块链和联邦学习的医疗健康数据共享模型

分析医疗健康数据应用情况,介绍区块链与联邦学习技术在医疗健康领域研究现状,提出基于区块链和联邦学习技术的健康医疗共享体系,阐述系统架构和应用流程,为实现医疗健康数据的安全可靠共享和智能处理提供新的解决方案。     

移动边缘计算环境下基于联邦机器学习的医联体慢病管理

介绍健康医疗数据应用发展情况及相关研究现状,提出采用联邦机器学习方式建立具有安全和隐私保护的慢病管理模型,从目的、系统架构、框架及功能、训练过程等方面阐述模型框架设计,指出其有助于大幅降低用户数据泄露风险。     

基于联邦特征学习的多机型低剂量CT重建算法

目的 提出一种基于联邦特征学习的多机型低剂量CT重建算法（FedCT）,以提升深度学习模型对多CT机型的泛化能力并保护数据隐私。方法 FedCT框架在每个协作学习的客户端中设置了一个基于数据-解析模型耦合驱动的Radon反变换智能重建模型作为局部网络模型,并采用投影域特异性学习策略,在局部投影域保留成像几何特异性。同时,引入联邦特征学习,使用条件特征参数标记局部数据并馈入网络模型进行编码以在图像域提升网络模型的泛化性。结果 在跨站点的多机型、多协议低剂量CT重建实验中,FedCT的重建结果在所有对比联邦学习方法中获得了最高的PSNR（高于次优的联邦学习方法+2.8048、+2.7301、+2.7263）、最高的SSIM指标（高于次优的联邦学习方法+0.0009、+0.0165、+0.0131）以及最低的RMSE指标（低于次优的对比联邦学习方法-0.6687、-1.5956、-0.9962）。在消融实验中,相较于一般联邦学习策略,采用投影特异学习策略的模型在测试集上的PSNR指标的Q1平均提升1.18,RMSE指标的Q3平均降低1.36。在引入联邦特征学习后,FedCT在测试集上的PSN...     

联邦学习在医疗信息化中的应用研究

介绍联邦学习技术分类和特点,阐述联邦学习技术在医疗信息化领域中的典型应用场景,包括心电异常检测、罕见病研究、老年人运动健康检测、医疗影像研究等方面,分析该领域面临的挑战和未来发展趋势。     

推进联邦学习技术在医学影像人工智能中的应用

医学影像的人工智能系统在辅助疾病诊疗方面已展现出巨大潜力,但医学影像数据孤岛、数据隐私安全及数据行业标准不统一等问题严重阻碍了人工智能赋能疾病诊疗。通过结合联邦学习和FAIR科学数据管理准则,可从技术上缓解上述问题对构建医学影像人工智能系统的影响,进而发挥多中心数据的最大价值,开发出更加高效、准确的疾病诊疗系统,指导基...     

基于思维导图的药学专业知识框架构建分析

思维导图是一种非常实用的思维工具,主要是使用一个关键词进行形象化的分类与构造,通过图文并茂的形式,将知识体系的主题关系层级呈现出来。在药学专业中,通过借助思维导图构建相应的知识框架,不仅便于学生理清学习思路,形成系统的知识结构,还能够促进学生提高学习效率。本文阐述了思维导图在药学专业各门课程中的应用,分析了基于思维导图构建相应课程知识框架的重要性,然后对基于思维导图的药学专业知识框架构建进行分析。     

德国的医疗卫生体制及对中国改革的启示

德国医疗卫生体制的基本框架医疗卫生服务体制德国的医疗卫生服务体制大致分为两个大的部分。一是以传染病控制为主的公共卫生体系,二是一般医疗服务体系。德国的公共卫生服务(主要指其传染病监测与控制体系,未包括环境卫生和职业卫生等方面的内容,下同)是由其三级政府(联邦、州和县)的卫生行政主管部门直接完成的,有自下而上的信息传递体系及反应和处理体系。在联邦一级,科赫     

中医辅助诊疗推荐系统设计与实现

目的 研究和开发辅助中医医生诊疗的中医辅助诊疗推荐系统.方法 以中医历史病案数据为基础,利用数据挖掘技术和度量学习技术挖掘、整理中医诊疗经验知识,建立病案相似度计算方法,设计中医辅助诊疗推荐系统功能框架并开发应用系统.结果 设计并构建了中医辅助诊疗推荐系统,在四诊识别阶段为医生推荐候选症状,在辨证论治阶段为医生推荐诊疗...     

框架式教学方法在生物化学教学中的应用

目的 评价护理专业生物化学教学中应用框架式教学方法的教学效果.方法 以张掖医学高等专科学校2008级护理专业316名学生为研究对象,其中,招收的理科学生158人,随机分为实验组和对照组,每组79人;招收的文科学生158人,随机分为实验组和对照组,每组79人.实验组采用框架式教学方法,对照组采用传统教学方法.结果 无论文科学生还是理科学生,其实验组学生在提高综合思维能力、提高对所学知识的应用能力、加强与临床的联系等方面均优于对照组学生,其差异均有统计学意义(P＜0.05).且框架式教学方法在提高理解能力、提高学习能力、提高学习生物化学兴趣三个方面,文科生的效果要优于理科生,其差异具有统计学意义(P＜0.05).结论 框架式教学方法在提高学生的综合能力、应用能力、学习能力和促进学生学习积极性等方面明显优于传统教学方法,尤其对文科生来说,效果更为明显.     

基于电子病历大数据的问答系统

基于电子病历大数据并结合信息检索与深度学习方法,设计一种辅助诊断问答系统。介绍系统的总体框架及电子病历数据库、价值网络、策略网络的设计,给出系统工作流程。该系统能帮助患者自查病情,也能为医生制定诊疗方案提供参考。     

联盟学习在生物医学大数据隐私保护中的原理与应用

结合2020年初新型冠状病毒疫情探讨数据分享与联合分析的必要性和紧迫性,系统介绍联盟学习技术原理和适用范围,根据不同数据类型特点详细阐述当前联盟学习技术在生物医疗大数据隐私保护中的应用及其与深度学习技术的结合。     

The Biomedical Research Hub: a federated platform for patient research data

ObjectiveThe objective was to develop and operate a cloud-based federated system for managing, analyzing, and sharing patient data for research purposes, while allowing each resource sharing patient data to operate their component based upon their own governance rules. The federated system is called the Biomedical Research Hub (BRH).Materials and MethodsThe BRH is a cloud-based federated system built over a core set of software services called framework services. BRH framework services include authentication and authorization, services for generating and assessing findable, accessible, interoperable, and reusable (FAIR) data, and services for importing and exporting bulk clinical data. The BRH includes data resources providing data operated by different entities and workspaces that can access and analyze data from one or more of the data resources in the BRH.ResultsThe BRH contains multiple data commons that in aggregate provide access to over 6 PB of research data from over 400 000 research participants.Discussion and conclusionWith the growing acceptance of using public cloud computing platforms for biomedical research, and the growing use of opaque persistent digital identifiers for datasets, data objects, and other entities, there is now a foundation for systems that federate data from multiple independently operated data resources that expose FAIR application programming interfaces, each using a separate data model. Applications can be built that access data from one or more of the data resources.     

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.     

WebDISCO: a web service for distributed cox model learning without patient-level data sharing

Objective The Cox proportional hazards model is a widely used method for analyzing survival data. To achieve sufficient statistical power in a survival analysis, it usually requires a large amount of data. Data sharing across institutions could be a potential workaround for providing this added power.Methods and materials The authors develop a web service for distributed Cox model learning (WebDISCO), which focuses on the proof-of-concept and algorithm development for federated survival analysis. The sensitive patient-level data can be processed locally and only the less-sensitive intermediate statistics are exchanged to build a global Cox model. Mathematical derivation shows that the proposed distributed algorithm is identical to the centralized Cox model.Results The authors evaluated the proposed framework at the University of California, San Diego (UCSD), Emory, and Duke. The experimental results show that both distributed and centralized models result in near-identical model coefficients with differences in the range 10^?15 to 10^?12. The results confirm the mathematical derivation and show that the implementation of the distributed model can achieve the same results as the centralized implementation.Limitation The proposed method serves as a proof of concept, in which a publicly available dataset was used to evaluate the performance. The authors do not intend to suggest that this method can resolve policy and engineering issues related to the federated use of institutional data, but they should serve as evidence of the technical feasibility of the proposed approach.Conclusions WebDISCO (Web-based Distributed Cox Regression Model; https://webdisco.ucsd-dbmi.org:8443/cox/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data.     

Federated Querying Architecture with Clinical &; Translational Health IT Application

We present a software architecture that federates data from multiple heterogeneous health informatics data sources owned by multiple organizations. The architecture builds upon state-of-the-art open-source Java and XML frameworks in innovative ways. It consists of (a) federated query engine, which manages federated queries and result set aggregation via a patient identification service; and (b) data source facades, which translate the physical data models into a common model on-the-fly and handle large result set streaming. System modules are connected via reusable Apache Camel integration routes and deployed to an OSGi enterprise service bus. We present an application of our architecture that allows users to construct queries via the i2b2 web front-end, and federates patient data from the University of Utah Enterprise Data Warehouse and the Utah Population database. Our system can be easily adopted, extended and integrated with existing SOA Healthcare and HL7 frameworks such as i2b2 and caGrid.     

Federated learning improves site performance in multicenter deep learning without data sharing

ObjectiveTo demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).Materials and MethodsDeep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.ResultsWe found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.DiscussionThe power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data.ConclusionFederated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.     

基于活动理论的网络学习策略教学模式初探

开展网络环境下学习策略教学是提高网络学习者学习能力,增强学习效果和效率的重要途径。文章从教师角度着眼,以活动理论为理论框架,针对网络高等教育,分析了网络学习环境下,学生策略学习活动系统构成及其教学模式。