医疗健康大数据发展现状研究

通过介绍国内外医疗健康大数据发展计划、学术组织、标准化组织、研究领域、研究项目和开放数据资源等相关主题,反映了医疗健康大数据的发展现状与动态,可为医疗健康大数据应用和研究提供参考。     

CNGBdb：国家基因库生命大数据平台

国家基因库生命大数据平台(China National GeneBank DataBase, CNGBdb)是一个致力于生命科学多组学数据归档和开放共享的数据库平台,是深圳国家基因库的核心功能"三库两平台"中生物信息数据库的对外服务平台,拥有深圳国家基因库丰富的样本资源、数据资源、合作项目资源和强大的数据计算和分析能力等优势。生命科学研究已经进入到了一个以高通量多组学数据为基础的大数据时代,迫切需要加强国际合作和信息共享。随着中国经济的发展和在生命科学研究领域的研究项目投入力度的加大,需要建立相关的生命大数据归档和共享的平台,来促进我国生命科学研究项目中生成的基因组学数据的系统管理、开放共享与合理利用。目前,CNGBdb主要提供生命科学研究相关的数据归档、知识搜索、数据管理、数据计算和数据服务等服务。其归档和共享的数据类型,主要包括项目、样本、实验、测序、组装、变异、序列等。截止2020年5月22号,CNGBdb已接受了全球生命科学科研工作者提交的研究项目达2176个,归档的基因组学数据量超过2221TB。未来,CNGBdb将继续推动生命科学研究多组学数据的开放共享和产业应用,完善基因组学数据的归档和共享功能,提升其服务生命科学数据开放共享的能力。CNGBdb的网址是:https://db.cngb.org/。     

利用语义网技术实现的分布式异构食品微生物数据整合

随着高通量测序技术的迅速发展和食品微生物研究的逐步深入,产生了大量的数据和知识,且以不同的数据格式分布在各种数据库中。为了更好地支持食品微生物的相关研究,从各种分布式、异构的数据和知识中,进行数据提取与转换,并形成一个整合的数据平台显得尤为重要。FoodMicrobes数据库利用语义网技术,建立了一个食品微生物的整合型数据平台。该平台从各种开放的公共数据库,提取了与食品微生物相关的基因、基因组、基因功能、蛋白质序列与结构、代谢途径、文献、专利等信息,利用RDF的方法,对数据进行转换,并建立了数据之间的关联,实现了数据整合,是目前在食品微生物领域以语义网方式建立的第一个数据库。在该平台中,实现了将食品微生物的物种、菌株层面的宏观信息与基因组、蛋白质、代谢与功能等微观层面信息的贯通,并通过友好的数据检索界面,为用户进行食品微生物研究提供了重要的工具。     

物联网环境下,智能医养融合电子平台设计

为缓解"医养融合"发展模式中养老者电子健康档案、电子病历实时传输和不同服务主体间信息共享的难题,本文提出智能医养融合电子平台模型。系统利用使用物联网技术的智能医疗设备采集养老者的生理信息数据,使用Map Reduce挖掘数据,快速有效的完成养老者生理数据的分析和整理。     

电子病历系统在医院信息管理系统中应用初探

电子病历系统是通过计算机等电子设备为载体,对医院患者的诊疗活动进行数字化记录的软件。电子病历中详细记录了医嘱、病程、过敏史、影像检查结果、出院记录等多项医疗数据。电子病历完整、系统、科学地记录了患者身体健康情况以及历次就诊记录,通过一个维度将患者内部不同层次的信息有机的联系在一起。与传统的纸张病历相比,电子病历可以迅速实现不同时间、不同医院医疗信息的高效整合以及信息共享,为临床诊疗提供大量科学准确的信息,大大提高医院的服务效率。本文通过电子病历系统在医院信息管理系统中的应用情况进行简要分析,以期提高电子病历系统在临床中解决实际医疗问题的能力。     

西安市雁塔区区域卫生分级诊疗信息平台建设探索

为响应国家卫计委全面推进分级诊疗工作的号召,解决百姓看病难、看病贵以及医疗资源不均衡的问题,西安市雁塔区作为陕西省城市医院分级诊疗首个试点,探索组建了以西安交通大学第一附属医院为核心的区域医疗联合体,并且将区域内各个医疗卫生机构的医疗资源整合在一起,搭建起地方区域分级诊疗信息共享平台。通过区域卫生分级诊疗信息平台的建立,实现了区域内医疗卫生机构基本业务信息系统数据的交换和共享,解决了医疗机构、卫生管理机构“信息孤岛”问题,建立起基层首诊、双向转诊、急慢分治、上下联动的分级诊疗模式,全面带动了陕西省医疗机构分级诊疗工作的开展。     

医疗大数据应用需求分析与平台建设构想

医疗大数据的应用对于临床医学研究、科学管理和医疗服务模式转型发展都具有重要意义。文章介绍了国内外医疗大数据应用现状，以及作者所在单位在医疗数据利用方面的做法经验，并从医务人员、患者、管理人员和科研人员的角度，分析了医疗大数据的应用需求。最后，结合已有实践，提出了医疗大数据应用平台的建设构想和步骤方法等。     

关于iFlora创建工作框架的建议

iFlora是依据传统植物分类学及相关学科的研究基础,融入现代DNA测序技术,应用高速发展的信息、网络技术及云计算分析平台,收集、整合和管理植物物种相关信息,以建成智能物种鉴定和数据提取的开放应用系统（智能装备）。通过与该系统的双向交流,一方面,可以不断整合新的数据和技术充实iFlora的内容和功能;另一方面,可以通过该系统的多种鉴定途径实现快速、准确和方便的物种鉴定,获取所需物种的相关信息,满足专业机构和公众对物种和生物多样性的认知要求。本文重点介绍了构成iFlora的应用装置和支撑该装置的实物库（凭证标本、分子材料和DNA库）的建设及其重要性;阐述了构成iFlora各单元的高度整合和集成的特点,以及基于计算机技术的物种信息数字化和开放的云计算数据分析处理服务平台的枢纽作用;并讨论了iFlora创建过程所面临的困难和挑战,以及拟研发的智能装备的框架和应用前景。     

遗传资源数字序列信息问题刍议

遗传资源数字序列信息(DSI)是测序技术的产物,至少包括DNA、RNA等遗传物质的序列信息和天然产物化学结构信息等,其获取和利用以及由此产生的利益分配问题已经成为《生物多样性公约》等国际进程的热点和焦点。自2016年以来,《生物多样性公约》框架下各方对此虽然开展了卓有成效的讨论,但在DSI内涵和外延、与遗传资源的关系、开放获取、监测DSI的利用等领域仍然存在根本分歧。DSI获取与惠益分享问题面临政治博弈、技术障碍、国内法与国际法协调、多公约协同等多重挑战。我国作为全球DSI的主要提供国和利用国,为有效应对DSI获取与惠益分享所带来的挑战和机遇,有必要加强以下方面的相关工作:(1)加强DSI的相关基础研究工作,特别是需要强化跨学科研究,并开展惠益分享试点示范;(2)适时制定生物信息数据管理制度,系统构建生物资源数据分类、汇交、共享、研究、利用、跨境传输、惠益分享等关键制度;(3)加快建成开放、安全、共享、互惠的全球性生物资源数据生产和存储基础设施,加强生物资源数据国际合作;(4)充分发挥诸如中国生物多样性保护国家委员会等跨部门协调机制作用,持续加强我国参与DSI相关国际论坛讨论的协同增效。     

第二类医疗技术准入管理问题与对策研究

医疗技术准入制度越来越受到重视,我国这项工作刚刚起步,大多数研究只探讨了高风险、涉及重大伦理问题的第三类医疗技术的临床应用以及伦理问题,而未对第二类医疗技术准入问题进行探讨。第二类医疗技术准入管理刚起步,并存在评价指标的选择问题、申报数据的可靠性和真实性问题及准入后的后期跟踪管理问题等。卫生行政部门必须在认真做好医疗信息公示的前提下,采用科学的评价指标,分步骤实施医疗技术准入管理,并切实做好准入后的跟踪管理。     

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools

The vast amount of data produced by today’s medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field.     

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.     

基于医学信息数据仓库模型的数据挖掘

利用数据仓库和数据挖掘技术,以现有医院信息系统HIS及医学信息资源为基础,基于PC和Windows操作系统,利用SQL Server2005及SQL Server 2005 Analysis Services（SSAS）等软件,搭建了医学信息数据仓库模型,并运用数据挖掘技术抽取数据库中数据隐藏的规律,提高医学信息的利用率。为从错综复杂的、庞大的医学信息库中提取有价值的决策支持信息提供有效的途径和方法。     

The use of information technology in improving medical performance. Part I. Information systems for medical transactions

Investment in medical information technologies reached $15 billion in 1996. However, these technologies have not had the wide impact predicted in streamlining bureaucracy, improving communications, and raising the effectiveness of care. In this series, we identify how such technologies are being used to improve quality and performance, the future directions for advancement, and the policy and research developments required to maximize public benefit from these technologies. Each of these articles focuses on a different type of information technology: (1) information systems to manage medical transactions; (2) physician-support technologies to improve medical practice; and (3) patient-focused technologies designed to change how people manage their own care. This first article of a 3-part series examines the successes of and opportunities for using advanced information systems that track and manage medical transactions for large populations to improve performance. Examples of such systems include: HEDIS, which gathers standardized data from health plans on quality of care; the USQA Health Services Research Program, which tracks treatment patterns and outcomes for 14 million insurance members; Ford's program to collect medical data for over 600,000 employees; and Harvard Pilgrim Health Care's system of computerized laboratory, pharmacy, ambulatory, and hospital admission records for its 1.5 million members. Data from these systems have led to modest improvements in knowledge and practice patterns for some diseases. Significant barriers are slowing efforts to add outcomes data to these databases and broaden the databases to cover larger populations. Nonetheless, existing data in currently evolving systems could be used to greater benefit in tracking public health and in identifying more effective treatments and causes of diseases.     

IMPAX PACS在黑龙江省区域化医疗远程会诊中的初步应用

目的:研究黑龙江省不同医疗机构之间新型协同服务模式,加强垦区各级医疗机构的信息化基础建设,建立基于医学影像存档与通信传输系统(Picture Archiving and Communications System,PACS)的数字化医疗区域。方法:将哈尔滨医科大学附属第四医院现有的影像数据归档,集成到IMPAX PACS数据中心(Internet Data Center,IDC),作为整个区域医疗的影像中心。通过IDC交换平台的延伸覆盖,以及医院信息系统(Hospital Information System,HIS)与XERO集成,可经网络调阅IDC中的影像,实现远程影像会诊。结果:建立基于IMPAX PACS的区域医疗;工程覆盖1家省会大医院和垦区2家综合性医院、5家二级医院、11家农场医院,实现联网医院间的影像学远程会诊。结论:PACS区域远程医疗系统的建立为基层百姓就医提供方便,影像学远程会诊可有效避免影像学重复检查,双向转诊、信息共享给患者带来更多的便利和实惠,具有巨大的社会效益。     

基于专利分析的医学科技重点技术前沿领域的识别研究

目的:采用定量分析与定性分析相结合的方法识别医学科技重点技术前沿领域。方法:在Thomson Innovation数据库中检索2006-2012年制药技术、生物医药技术和医疗器械三个领域申请的专利数量,采用专利计量分析、统计学趋势分析等定量分析方法结合专家专业知识的定性分析方法对检索、采集的专利进行分析并识别前沿领域。前沿领域的选择原则是专利申请数量标化平均值较高(制药技术、生物医药技术和医疗器械领域标化平均值分别选择大于10%、15%和20%的子领域),或者有显著性增长趋势(P0.05且b0)。结果:本研究共识别医学科技重点技术前沿领域12个,其中制药技术前沿领域4个,分别是:天然产物,普通药,杂环化合物和其他有机物;生物医药技术前沿领域4个,分别是:疾病诊断和检测,基因治疗和核苷类药物,蛋白多肽类药物和生物合成技术;医疗器械前沿领域4个,分别是:电子医疗设备,诊断及手术设备,消毒、注射及电疗设备和医疗救助及口服设备。结论:通过定量与定性分析方法相结合共识别医学科技重点技术前沿领域12个,对专利进行多角度分析可以全面把握前沿领域的技术发展方向和特点,为我国医学科技政策的制定提供基于数据和事实的信息支撑。     

民族药用植物指纹数据库的构建研究

通过对四川省时采集的民族药用植物标本进行基因和化学指纹图谱、药用价值、民族传统利用等信息的整理,用SQL Server 2000建立一个民族药用植物指纹的开放式关系数据库初步框架。该数据库重点体现药用植物的民族利用特色,同时包括各种指纹图谱图片。该数据库不仅可以快速、方便地检索和查询有关民族药用植物的背景资料、药用价值以及指纹图谱信息,而且可为四川省内民族药用植物资源的保护和利用提供基础。     

C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR

A renewed interest by consumer information technology giants in the healthcare domain is focused on transforming smartphones into personal health data storage devices. With the introduction of the open source ResearchKit, Apple provides a framework for researchers to inform and consent research subjects, and to readily collect personal health data and patient reported outcomes (PRO) from distributed populations. However, being research backend agnostic, ResearchKit does not provide data transmission facilities, leaving research apps disconnected from the health system. Personal health data and PROs are of the most value when presented in context along with health system data. Our aim was to build a toolchain that allows easy and secure integration of personal health and PRO data into an open source platform widely adopted across 140 academic medical centers. We present C3-PRO: the Consent, Contact, and Community framework for Patient Reported Outcomes. This open source toolchain connects, in a standards-compliant fashion, any ResearchKit app to the widely-used clinical research infrastructure Informatics for Integrating Biology and the Bedside (i2b2). C3-PRO leverages the emerging health data standard Fast Healthcare Interoperability Resources (FHIR).     

Electronic Medical Records and Electronic Health Records: Overview for Nurse Practitioners

Electronic medical records (EMRs) and electronic health records (EHRs) have become essential systems by which nurse practitioners (NPs) communicate vital patient information to other members of the health care team as well as to patients. In this article we examine the important distinctions between EMRs and EHRs; review the genesis of these types of records; summarize applicable provisions of the Health Insurance Portability and Accountability Act from a recent legal case centered around NP utilization of EMRs and EHRs; address open patient access to medical information; and examine threats to security. Suggestions are offered on ways in which NPs can safeguard confidential patient information.     

Grid computing in image analysis

Diagnostic surgical pathology or tissue–based diagnosis still remains the most reliable and specific diagnostic medical procedure. The development of whole slide scanners permits the creation of virtual slides and to work on so-called virtual microscopes. In addition to interactive work on virtual slides approaches have been reported that introduce automated virtual microscopy, which is composed of several tools focusing on quite different tasks. These include evaluation of image quality and image standardization, analysis of potential useful thresholds for object detection and identification (segmentation), dynamic segmentation procedures, adjustable magnification to optimize feature extraction, and texture analysis including image transformation and evaluation of elementary primitives. Grid technology seems to possess all features to efficiently target and control the specific tasks of image information and detection in order to obtain a detailed and accurate diagnosis. Grid technology is based upon so-called nodes that are linked together and share certain communication rules in using open standards. Their number and functionality can vary according to the needs of a specific user at a given point in time. When implementing automated virtual microscopy with Grid technology, all of the five different Grid functions have to be taken into account, namely 1) computation services, 2) data services, 3) application services, 4) information services, and 5) knowledge services. Although all mandatory tools of automated virtual microscopy can be implemented in a closed or standardized open system, Grid technology offers a new dimension to acquire, detect, classify, and distribute medical image information, and to assure quality in tissue–based diagnosis.