医学数据资源共享中数据元著录的标准化方法研究

目的研究对数据元进行著录的标准化方法.方法首先建立数据元结构术语条目库,然后选择结构术语条目组成完整的数据元,最后进行数据元属性著录.结果根据数据元的定义、构成以及国家标准设计了一种数据元标准化著录方法.结论 对数据元进行标准化著录是医学数据资源共享的重要工作,需要按照相关标准选择合理的方法进行.     

数据研究在成分献血招募工作中的作用

目的通过长时间、全面地收集献血者采供血数据,为成分献血招募工作的方法和方向提供指导意见,使成分献血工作取得事倍功半的效果。方法根据成分献血招募工作的特点和需要,收集献血者、采供血的相关数据,再进行及时、适当的分析和统计,并根据结果判断招募方法和方向是否有效。结果每年成分献血采集量从2002年的56袋增加到2010年的19 153袋,成分比例从2006年的1.07%增加到2010年的25.13%。结论全面的数据收集,恰当的分析和统计,在成功招募、保留成分献血者中起到重要作用。     

人工智能在检验医学发展中的重要作用

检验大数据涉及全身各系统并随着疾病的变化而变化,纵横交错,导致目前我们还没有突破检验结果综合分析的瓶颈。借助人工智能,通过检验大数据处理,根据疾病特点对检验数据结果进行全面的综合分析,通过模拟、延伸和扩展将检验数据与疾病的诊断、鉴别诊断、治疗效果评价和预后判断联系起来,产生具有最高水平的智能分析,突破人脑对巨大数据同时...     

国际脊髓损伤数据集之核心数据集

[编者按]脊髓损伤的临床研究尚有很长的路要走,如果相关领域的专业人员都能按统一的标准收集临床资料,就便于比较不同地区、作者所发表的研究结果,更利于达到成功的目标. 国际脊髓协会联合数十个相关国际专业组织建立了脊髓损伤核心数据集、下尿路功能障碍数据集、尿动力学数据集、尿道影像数据集、肠功能基础数据集、肠功能扩展数据集、疼痛基础数据集,这些数据集的建立为全球脊髓损伤工作者采用统一的标准收集临床资料和报告研究成果建立了基础.     

国际脊髓损伤数据集:疼痛基础数据集

慢性疼痛足降低脊髓损伤患者生活质量的最常见原因之一。目前仍缺乏统一的收集疼痛数据的标准方法。采用统一的办法收集脊髓损伤患行的疼痛信息将有利于疼痛产生机制的研究及治疗结果的比较。本文旨在通过设立疼痛基础数据集来对脊髓损伤患者疼痛信息的收集和报告标准化。文中对数据集的变量进行了详细说明,并通过3个训练案例解释如何填写基础数据集表。     

院前急救在医学中的作用

现代医学进步的一个显著标志是急危重患者救治水平的提高,同时衡量一个医院的整体医疗水平,往往也是看其危重患者的处理结果。国际上先进国家建立了急救医疗服务体系(EMSS)即将院前急救-急诊室急救-24 h救治形成一个完整体系[1]。这是急救医学的一大进步,是社会发展的必然趋势。院前急救是     

大数据与人工智能在医学领域的应用进展

随着信息技术在医疗行业的发展,以非结构化数据为主的医疗数据急剧增长,利用大数据技术对其进行处理,可生成不同主题的数据库,创造巨大的附加价值。同时,以深度学习为核心的人工智能技术迅猛发展,人工智能辅助诊疗迎来了巨大的发展空间,有助于指导医疗活动,提高医疗诊治效率。大数据、人工智能与医疗行业的结合将成为未来重要的发展方向。本文就大数据与人工智能在医疗行业的应用进展进行阐述。     

结核病监测最小数据集的筛选与结果分析

目的 基于结核病监测工作现状,开展结核病监测最小数据集的研究.方法 采用德尔菲法确定结核病监测最小数据集的内容.结果 结核病监测最小数据集共有70个数据元,包括个人基本信息、病例报告信息、检验检测信息、流行病学调查信息、治疗用药信息和随访管理信息6部分内容.结论 构建的结核病监测最小数据集科学、合理,为规范结核病监测数...     

量子医学在恶性肿瘤监测中的作用研究

目的：探讨量子医学在恶性肿瘤的筛查及治疗后病情随访等的临床价值。方法：用量子共振检测仪（QRS）测定95例正常体检者，15例良性肿瘤患者和130例恶性肿瘤患者头发中的新生物及恶性新生物的代码量值。结果：新生物及恶性新生物的代码量值在恶性肿瘤患者与正常体检者之间比较有极显著差异（P〈0．01），此检测手段检测恶性肿瘤的灵敏度为85．6％，特异性为98．5％，假阳性率为1．47%，假阴性率为14．42%，符合率为91．8％。结论：量子共振检测作为恶性肿瘤早期筛查及治疗后病情随访的一种手段具有较大临床参考意义。     

Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository

Background  Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium''s partner sites. Objectives  Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods  Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results  The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats . Conclusion  The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.     

数字人体空间数据的Metadata

笔者首先介绍了Metadata的发展背景和现状;然后研究了数字人体空间数据Metadata的理论基础、体系结构;最后阐述了数字人体空间数据Metadata的实现和今后研究的趋势.     

Developing the Minimum Dataset for the New Mexico Decedent Image Database

Background  A minimum dataset (MDS) can be determined ad hoc by an investigator or small team; by a metadata expert; or by using a consensus method to take advantage of the global knowledge and expertise of a large group of experts. The first method is the most commonly applied. Objective  Here, we describe a use of the third approach using a modified Delphi method to determine the optimal MDS for a dataset of full body computed tomography scans. The scans are of decedents whose deaths were investigated at the New Mexico Office of the Medical Investigator and constitute the New Mexico Decedent Image Database (NMDID). Methods  The authors initiated the consensus process by suggesting 50 original variables to elicit expert reactions. Experts were recruited from a variety of scientific disciplines and from around the world. Three rounds of variable selection showed high rates of consensus. Results  In total, 59 variables were selected, only 52% of which the original resource authors selected. Using a snowball method, a second set of experts was recruited to validate the variables chosen in the design phase. During the validation phase, no variables were selected for deletion. Conclusion  NMDID is likely to remain more “future proof” than if a single metadata expert or only the original team of investigators designed the metadata.     

《护理与康复》杂志社网站开通公告

<正>为使《护理与康复》杂志跟上网络化步伐,缩短读者、作者、编者的距离,为国内外临床护士、研究人员以及医学院校从事护理教育工作者提供快捷的学术交流平台,《护理与康复》网站于2014年10月1日正式开通,网址:www.zjhlykf.com。网站依托杂志本身,体现传递护理学术信息,提高理论与技术水平,促进护理学科发展的办刊宗旨,通过期刊简介、期刊荣誉、投稿指南、审稿指南、期刊订阅、在线留言、期     

医药卫生科学数据分类与编码的研究

目的研究医药卫生科学数据的分类与编码方法,以保证科学数据得到有效的管理.方法采用线分类法,建立医药卫生科学数据分类框架;使用组配技术进行编码,实现体系分类法的组配化,建立医药卫生科学数据的多轴分类集合.结果建立了医药卫生科学数据分类框架;确立了医药卫生科学数据分类框架类目内容;设计了医药卫生科学数据编码方案.结论分类编码研究实现了针对数据的归并和信息的组织.     

组配法在医药卫生科学数据分类编码中的应用

目的探讨组配方法在医药卫生科学数据分类与编码中的应用,建立医药卫生科学数据的多轴分类集合,实现科学数据的规范表达和有效管理.方法采用组配方法,对医药卫生科学数据设计分段编码方案.结果主分类表设计5个码位,表示三个层级,即门类（第一位）字母码、亚门类（第二、三位）数字码和大类（第四、五类）数字码;复分类表等同采用既定分类方法中的分类编码体系.结论组配法与传统分类法相结合可实现分类法等级列举式向分面组配式发展,从而达到对医药卫生科学数据归并和信息组织的目的.     

National Emergency Medical Services Information System (NEMSIS)

The absence of emergency medical services (EMS) patient care data has hindered development andevaluation of EMS systems. The National Highway Traffic Safety Administration (NHTSA), in cooperation with the Health Resources andServices Administration (HRSA), has provided funding to the National Association of State EMS Directors to develop a National EMS Information System (NEMSIS). NEMSIS is being designed to provide a uniform national EMS dataset, with standard terms, definitions, andvalues, as well as a national EMS database, with aggregated data from all states on a limited number of data elements. Forty-eight of the states, the District of Columbia, andthree territories signed a memorandum of agreement documenting support for the NEMSIS project andexpressing a desire for full implementation of the NEMSIS dataset. NHTSA has agreed to house the National EMS Database at its National Center for Statistics andAnalysis. NHTSA, in cooperation with HRSA andthe Centers for Disease Control andPrevention, recently entered into a cooperative agreement with the University of Utah School of Medicine to operate a NEMSIS Technical Assistance Center that will provide related assistance to official EMS agencies andto commercial software vendors. The Technical Assistance Center will also biannually assess state andterritorial capabilities to provide data to the national EMS database. NEMSIS will provide a uniform national EMS dataset, with standard terms, definitions, andvalues, as well as a national EMS database, with aggregated data from all states on a limited number of data elements. Many of the potential benefits of implementation of NEMSIS are enumerated in this report.     

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

Objective  The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods  Studies were included if they were fully published articles that used machine learning and implemented a procedure to mitigate the effects of temporal dataset shift in a clinical setting. We described how dataset shift was measured, the procedures used to preserve model performance, and their effects. Results  Of 4,457 potentially relevant publications identified, 15 were included. The impact of temporal dataset shift was primarily quantified using changes, usually deterioration, in calibration or discrimination. Calibration deterioration was more common ( n  = 11) than discrimination deterioration ( n  = 3). Mitigation strategies were categorized as model level or feature level. Model-level approaches ( n  = 15) were more common than feature-level approaches ( n  = 2), with the most common approaches being model refitting ( n  = 12), probability calibration ( n  = 7), model updating ( n  = 6), and model selection ( n  = 6). In general, all mitigation strategies were successful at preserving calibration but not uniformly successful in preserving discrimination. Conclusion  There was limited research in preserving the performance of machine learning models in the presence of temporal dataset shift in clinical medicine. Future research could focus on the impact of dataset shift on clinical decision making, benchmark the mitigation strategies on a wider range of datasets and tasks, and identify optimal strategies for specific settings.