Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.     

Clustering of autoimmune disease in parents of siblings from the Type 1 diabetes Warren repository.

AIMS: Autoimmune disorders co-exist in the same individuals and in families, implying a shared aetiology. The aim of this study was to compare the prevalence of the common autoimmune diseases in the parents of siblings from the Type 1 diabetes Warren repository with the general population. METHODS: Between 1989 and 1996, 505 British families with at least two siblings affected by Type 1 diabetes were recruited. Clinical information was collected regarding the presence of autoimmune disease in the parents and the prevalence of disease in the parents was compared with that expected in the general population. RESULTS: The prevalence of autoimmune disease in the parents was significantly higher in the repository compared with that expected in the general population [P-value = 1.98 x 10(-5) (female), P-value = 1.1 x 10(-8) (male)]. Type 1 diabetes was recorded in 63/1010 (6.2%) parents with a marked paternal preponderance (9.5 vs. 3%P = 0.002). Other autoimmune diseases affected 27% of parents with diabetes and 13.2% of parents without diabetes (P < 0.01). CONCLUSION: These data confirm the importance of family history as a significant risk factor for the development of Type 1 diabetes and support the hypothesis that the common autoimmune diseases share at least some aetiological mechanisms.     

数据期刊科学数据质量评审主要问题研究——以《全球变化数据仓储电子杂志(中英文)》为例

【目的】 分析数据期刊科学数据质量评审中发现的主要问题,为促进科学数据质量提升,建立数据期刊质量评审行业标准和规范提供参考与借鉴。【方法】 以《全球变化数据仓储电子杂志(中英文)》的数据质量评审实践为例,对元数据质量评审、实体数据质量评审、元数据-实体数据-数据论文初稿对照评审3个方面发现的主要数据质量问题进行归纳和总结,并对其原因进行分析。【结果】 数据质量的主要问题有:元数据内容不完整,表达不规范;实体数据和元数据、数据论文中的数据描述不一致;实体数据的研发方法有疏漏,或缺少科学依据;实体数据与实际情况不符;实体数据内容不完整;实体数据内容不一致;引用实体数据不标注出处,或者标注不规范。【结论】 目前数据质量评审发现的问题比较多,这与当前的科研评价机制、部分作者对待数据缺乏严谨态度等有密切关系。     

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform: Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management. It is a common requirement to reuse the data for clinical research. However, we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform. In this paper, we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology. We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform. Secondly, we built special disease case repositories (i.e., heart failure repository) that utilize the graph to search the related patients and to normalize the data. Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure, we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository. After the propensity score matching, the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired. Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients. This paper presents the workflow and application example of big data mining based on regional EHR data.     

医学实验室结果审核和解释系统的设计与实现

智能化是实验室信息系统发展的重要方向。开发医学实验室结果审核和解释系统，可提高实验室的工作效率和质量，并提供检验结果解释和咨询服务。该系统利用基于规则的专家系统实现对检验结果的审核及解释，通过差值校验、比值校验、界限校验、患者数据质控等专用算法来提高审核质量。系统设计了审核和解释的绝大部分规则的通用推理机，能同时进行多个患者结果的推理运算，可用于临床辅助医学决策。     

The National Institutes of Health’s Biomedical Translational Research Information System (BTRIS): Design,contents, functionality and experience to date

The US National Institutes of Health (NIH) has developed the Biomedical Translational Research Information System (BTRIS) to support researchers’ access to translational and clinical data. BTRIS includes a data repository, a set of programs for loading data from NIH electronic health records and research data management systems, an ontology for coding the disparate data with a single terminology, and a set of user interface tools that provide access to identified data from individual research studies and data across all studies from which individually identifiable data have been removed. This paper reports on unique design elements of the system, progress to date and user experience after five years of development and operation.     

分级存储技术在电子病历无纸化存储中的应用

为解决电子病历无纸化存储的空间、效率、效益问题，提出一种基于分级存储技术的解决方案。介绍分级存储技术的概念、存储方式、必要性、存储策略、设备及技术优势，并以中山市人民医院为例对上述方法的有效性和实用性进行验证。     

GaitaBase: Web-based repository system for gait analysis

The need to share gait analysis data to improve clinical decision support has been recognised since the early 1990s. GaitaBase has been established to provide a web-accessible repository system of gait analysis data to improve the sharing of data across local and international clinical and research community. It is used by several clinical and research groups across the world providing cross-group access permissions to retrieve and analyse the data. The system is useful for bench-marking and quality assurance, clinical consultation, and collaborative research. It has the capacity to increase the population sample size and improve the quality of ‘normative’ gait data. In addition the accumulated stored data may facilitate clinicians in comparing their own gait data with others, and give a valuable insight into how effective specific interventions have been for others.     

搜索引擎的渐增式爬行和备份式更新模式

介绍了搜索引擎的总体结构,分析了搜索引擎中爬行器的爬行策略和网页库的更新模式。介绍了其中一种较为合理的爬行和更新模式及其实现技术,实现了渐增式地爬行高质量网页和提高网页库新鲜度的目的。