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。     

基于机器学习的肠道菌群数据建模与分析研究综述

人体肠道菌群与人类的健康和疾病存在密切关系,对肠道菌群的宏基因组数据进行建模和分析,在疾病预测及诊断相关领域科学研究和社会应用方面均具有重要意义。本文从大数据分析和机器学习的角度,对人体肠道菌群数据的建模、分析和预测算法的原理、过程以及典型研究应用实例进行综述,以期推动肠道菌群分析相关研究发展以及探索结合机器学习算法进行肠道菌群分析的有效方式,同时也为开发基于肠道菌群数据的新型诊疗手段提供借鉴,推动我国精准医疗事业发展。     

公共卫生大数据研究进展——生物信息的新领域

大数据时代的公共卫生面临新的机遇和挑战。为了推动公共卫生大数据的应用,准确把握其内涵,开发针对性的解决方案,达到改善人们健康状况的目的,基于此对公共卫生大数据的现状进行了分析和论证。研究表明:通过对多个不同来源公共卫生数据进行收集和整理,能够形成公共卫生大数据,通过深入挖掘和分析,能够获取重大疾病影响因素、流行病的传播规律等信息,帮助医疗卫生人员和相关机构进行预测和评估,以便采取有效的管理手段和措施,保护人民健康,减少医疗花费。发现通过同生物信息技术相结合,公共卫生数据的获取、管理、分析、安全和应用方面都会有很大的发展空间。认为计算机技术的进一步应用,针对性的大数据挖掘方法开发,以及新型公共卫生人才培养,是发展这一领域的关键因素。     

我国医疗卫生资源配置现状与政策建议

作为有限的公共资源之一的卫生资源,其配置状况直接影响卫生服务需求及利用,本文重点分析了影响医疗服务公平性和可及性相关的宏观背景、发展趋势和主要问题,提出战略建议如下：落实公立医疗机构改革,发挥示范作用;规范“互联网+医疗流程”模式,促进传统医疗资源共享,提高健康大数据利用效率;推进紧密型医疗集团发展,建立柔性的分级诊疗制度;构建全科医生生态区域和健康管理网络,实现资源下沉;试行第三方影像检验平台等新型服务模式,推进均质服务。     

2019年启航献礼

精准医疗是应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前火热的大数据技术、智能化技术等,结合患者生活环境和临床数据,实现精准的疾病分类和诊断,制定具有个性化的疾病预防和诊疗方案,包括对风险的精确预测、疾病精确诊断、疾病精确分类、药物精确应用、疗效精确评估、疗后精确预测等。精准医疗是医学自身发展的客观必然,是人民群众对健康新需求的使然。精准医疗的核心价值是造福于患者,造福于人类,尤其是在当今中国,人民生活水平普遍得到改善和提高,人民对健康的追求达到了一个新的高度。     

生命与健康大数据中心资源

生命与健康多组学数据是生命科学研究和生物医学技术发展的重要基础。然而,我国缺乏生物数据管理和共享平台,不但无法满足国内日益增长的生物医学及相关学科领域的研究发展需求,而且严重制约我国生物大数据整合共享与转化利用。鉴于此,中国科学院北京基因组研究所于2016年初成立生命与健康大数据中心(BIG Data Center, BIGD),围绕国家人口健康和重要战略生物资源,建立生物大数据管理平台和多组学数据资源体系。本文重点介绍BIGD的生命与健康大数据资源系统,主要包括组学原始数据归档库、基因组数据库、基因组变异数据库、基因表达数据库、甲基化数据库、生物信息工具库和生命科学维基知识库,提供生物大数据汇交、整合与共享服务,为促进我国生命科学数据管理、推动国家生物信息中心建设奠定重要基础。     

健康医疗可穿戴设备数据隐私相关问题研究

归纳、总结健康医疗可穿戴设备采集的数据内容,重点绘制健康医疗可穿戴设备的数据流动环节,并将数据流动划分为采集、上传、集成交互以及信息反馈等主要环节,并分析各主要环节中以及其他方面存在及潜在的数据安全与隐私问题,希望能为健康医疗可穿戴设备的数据隐私保护机制提供不同角度的理论参考。     

精准医疗与伴随诊断产业发展研究

<正>1概述1.1精准医疗精准医疗被誉为医疗的未来,它基于人们在基因、环境、工作与生活方式的个体差异,应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前热门的大数据技术、人工智能技术等,通过对患者的生活环境和临床数据等进行分析,实现精准的疾病分类和诊断,制订具有个性化的疾病预防和诊疗方案。精准医疗包括对风险的精确预测、疾病精确诊断、疾病精确分类、药物精确应用、疗效精确评估与疗后精确预测。精准医疗是科学技术及医学自身发展的客观必然,也是广大民众摆脱贫困以后对健康、教育、生活质量追求的必然。因     

重大心血管疾病:遗传和环境的影响及防控策略

心血管疾病在全球死亡病因和疾病负担中位列首位,冠心病、脑卒中等重大心血管疾病在我国造成巨大疾病负担和社会经济负担.心血管疾病的发生,受遗传和环境因素共同影响.国内外基因组学研究已经鉴定了心血管疾病及血压和血脂等表型的遗传特征谱,为多组学大数据整合、精准医疗及个性化防治奠定了基础.通过长期、大规模队列和人群防治研究,明确了影响心血管疾病发生的主要危险因素,创建了适于国人的心血管疾病风险评估模型.当今,国家高度重视并实施健康战略,我国重大心血管疾病的病因探索和防控迎来前所未有的机遇.政策引领、"互联网+"健康产业的发展、创新性研究与人群防治,都将助力心血管疾病防控,促进国人健康水平的提升.     

综合医院大数据应用需求调查与分析

目的 分析综合医院对于大数据应用的内在需求,为医院的大数据研发与应用提供导向和依据。方法 采用德尔菲法自制医院大数据应用需求调查问卷,随机抽取中国研究型医院学会医疗分会64家会员单位进行调查,获得有效问卷104份,有效回收率为94.55%。结果 精准医疗（4.31±0.42）分,精益管理（4.23±0.56）分,科学研究（4.19±0.52）分,健康管理（4.16±0.52）分,数字医疗（4.06±0.60）分,教育培训（3.69±0.69）分。不同性别、年龄、职称、岗位组间的需求差异有统计学意义（P＜0.05）。多元线性回归分析结果显示,医学人工智能（b=0.324,P=0.000）和互联网+医疗（b=0.161,P=0.047）的需求程度会对医院大数据应用前景态度产生显著的正向影响关系。结论 综合性医院对大数据具有较强的、多样化的应用需求,应以实际需求为导向,重点推进精准医疗、医学人工智能和互联网+医疗等相关应用的研发。     

大数据在生态环境领域的应用进展与展望

随着互联网和移动通讯技术的发展,生态环境领域从信息采集到加工处理也进入信息化和数字化时代,数据量呈现爆发式增长,生态环境大数据受到越来越多的关注.生态环境大数据是在对生态环境要素“空天地一体化”连续观测的基础上,集成海量的多源多尺度信息,借助云计算、人工智能及模型模拟等大数据分析技术,实现生态环境大数据的集成分析和信息挖掘.生态环境大数据存在数据来源多样、涉及部门广;数据采集方式不统一;服务对象众多、对专业化服务要求高等特点.大数据已在生态环境领域得到了初步应用,如在全球气候变化预测、生态网络观测与模拟和区域大气污染治理等方面作用明显.目前我国生态环境大数据的发展还存在诸多问题,包括数据共享难、监测技术落后、传感器等监测设备严重依赖进口、数据集成和深度分析能力不足等.随着大数据技术的进步,未来大数据在解决生态环境健康问题、提高重大生态环境风险预警预报水平、提高生态环境领域科学研究水平等方面都将发挥巨大作用.大数据将最终实现生态环境管理决策定量化、精细化,生态环境信息服务多样化、专业化和智能化,为中国社会经济可持续发展和生态文明建设提供技术保障.     

The Scale of Faith Based Organization Participation in Health Service Delivery in Developing Countries: Systemic Review and Meta-Analysis

The extent of faith-based organizations'' participation within the overall health systems of developing countries is unclear. Recent reports state that faith-based organizations play a substantial role in providing healthcare in developing countries, cited in some publications as up to 70% of all healthcare services. The data behind these numbers are sometimes difficult to pinpoint and seem at odds to national and regional survey data. In an effort to quantify the contribution of faith-based organizations to healthcare delivery in low- and middle-income countries, we undertook a systematic review of the literature and conducted a new analysis of relevant Demographic and Health Survey data from 47 countries. Our findings demonstrate that the magnitude of healthcare provided by faith-based organizations may be lower than previously estimated. Understanding the scale of FBO-provided medical care is important for health sector planning, and more accurate and complete estimates are needed.     

Translational Informatics for Parkinson’s Disease: from Big Biomedical Data to Small Actionable Alterations

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.     

Enterprise imaging and big data: A review from a medical physics perspective

In recent years enterprise imaging (EI) solutions have become a core component of healthcare initiatives, while a simultaneous rise in big data has opened up a number of possibilities in how we can analyze and derive insights from large amounts of medical data. Together they afford us a range of opportunities that can transform healthcare in many fields. This paper provides a review of recent developments in EI and big data in the context of medical physics. It summarizes the key aspects of EI and big data in practice, with discussion and consideration of the steps necessary to implement an EI strategy. It examines the benefits that a healthcare service can achieve through the implementation of an EI solution by looking at it through the lenses of: compliance, improving patient care, maximizing revenue, optimizing workflows, and applications of artificial intelligence that support enterprise imaging. It also addresses some of the key challenges in enterprise imaging, with discussion and examples presented for those in systems integration, governance, and data security and privacy.     

Biomedical Big Data: New Models of Control Over Access,Use and Governance

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it.     

Healthcare organizations and high profile disagreements

In this paper, we examine healthcare organizations’ responses to high profile cases of doctor–parent disagreement. We argue that, once a conflict crosses a certain threshold of public interest, the stakes of the disagreement change in important ways. They are no longer only the stakes of the child’s interests or who has decision-making authority, but also the stakes of public trust in healthcare practitioners and organizations and the wide scale spread of medical misinformation. These higher stakes call for robust organization-level responses. There are responsible and thoughtful ways for healthcare organizations to directly engage with these cases. Hospitals should seek an alliance with the parents around the goal of public discussion and utilize web-based platforms to provide the public with information about medical conditions, experimental treatments, and how clinical ethics deliberation in hospitals works. We outline five important lessons for healthcare organizations to keep in mind when responding to such cases. Approached with care, these cases could become “teachable moments” for both healthcare organizations and society.     

A review of big data analysis methods for baleen whale passive acoustic monitoring

Many organizations collect large passive acoustic monitoring (PAM) data sets that need to be efficiently and reliably analyzed. To determine appropriate methods for effective analysis of big PAM data sets, we undertook a literature review of baleen whale PAM analysis methods. Methodologies from 166 studies (published between 2000–2019) were summarized, and a detailed review was performed on the 94 studies that recorded more than 1,000 hr of acoustic data (“big data”). Analysis techniques for extracting baleen whale information from PAM data sets varied depending on the research observed. A spectrum of methodologies was used and ranged from manual analysis of all acoustic data by human experts to completely automated techniques with no manual validation. Based on this assessment, recommendations are provided to encourage robust research methods that are comparable across studies and sectors, achievable across research groups, and consistent with previous work. These include using automated techniques when possible to increase efficiency and repeatability, supplementing automation with manual review to calculate automated detector performance, and increasing consistency in terminology and presentation of results. This work can be used to facilitate discussion for minimum standards and best practices to be implemented in the field of marine mammal PAM.     

国家级生物大数据中心展望

大数据时代下,科学大数据已经成为科技创新和社会经济发展的新动力。我国是生物数据生产大国,生命大数据是人口健康和国家安全的重要战略资源。面对我国生物数据因存储零散、缺乏系统监管而大量丢失和流失,以及严重依赖国际生物组学大数据中心的局面,亟需从国家层面建设我国自己的生命大数据保存和管理体系。本文以美国NCBI为例介绍了国际生物大数据中心的发展历程及现状,阐明我国建立国家级生物大数据中心的重要性、迫切性、当前历史机遇和发展前景。中国科学院北京基因组研究所生命与健康大数据中心为此做了大量努力,并在数据存储、汇交和转化应用上取得了阶段性成果,以期推进我国生物大数据中心的建设,提高生命科学研究的国际竞争力和影响力。     

The use of Ethics Decision‐Making Frameworks by Canadian Ethics Consultants: A Qualitative Study

In this study, Canadian healthcare ethics consultants describe their use of ethics decision‐making frameworks. Our research finds that ethics consultants in Canada use multi‐purpose ethics decision‐making frameworks, as well as targeted frameworks that focus on reaching an ethical resolution to a particular healthcare issue, such as adverse event reporting, or difficult triage scenarios. Several interviewees mention the influence that the accreditation process in Canadian healthcare organizations has on the adoption and use of such frameworks. Some of the ethics consultants we interviewed also report on their reluctance to use these tools. Limited empirical work has been done previously on the use of ethics decision‐making frameworks. This study begins to fill this gap in our understanding of the work of healthcare ethics consultants.