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.     

基于现代医案挖掘中医药治疗慢性阻塞性肺疾病的证治规律

目的 基于文献医案借助中医传承辅助平台（V2.5）挖掘中医药治疗慢性阻塞性肺疾病（COPD）的证治规律，为中医药治疗COPD提供新思路。方法 检索中国知网（CNKI）、万方数据库（WanFang DATA）、维普中文科技期刊数据（VIP）等关于中医治疗COPD的医案，将符合纳入文献的医案整理在单独的Word文档中，经数据规范化后逐一录入中医传承辅助平台（V2.5），借助此软件携带的算法对所纳入COPD医案的“症状、中医证型、中药及中药性味归经”进行频数统计及对纳入医案的“组方规律”进行关联分析并挖掘出治疗COPD潜在的核心药对及新方。结果 共纳入103篇文献，共收集医案126个医案，共131次诊疗。经统计COPD常见的症状有咳嗽、咳痰、胸闷、气喘等；常见的中医证型有痰热壅肺证、痰瘀阻肺、肺脾气虚等；常见的中医治法有清热化痰、培土生金、降逆平喘等；其中治疗COPD的高频中药有半夏、苦杏仁、麻黄、陈皮等；常用药对组合有细辛-半夏、半夏-五味子-甘草等；并挖掘出12首治疗COPD的新方药。结论 中医认为COPD是由外邪、痰、瘀等病理因素相互形成的以咳、痰、喘为主要症状的肺系疾病，其主要的证候有痰热壅肺证、痰瘀阻肺、肺脾气虚等，“扶正祛邪”是其治疗的基本原则，在临床应采用祛痰化瘀，补益肺脾或肺肾的中医治法治疗COPD。     

Semantics based approach for analyzing disease-target associations

BackgroundA complex disease is caused by heterogeneous biological interactions between genes and their products along with the influence of environmental factors. There have been many attempts for understanding the cause of these diseases using experimental, statistical and computational methods. In the present work the objective is to address the challenge of representation and integration of information from heterogeneous biomedical aspects of a complex disease using semantics based approach.MethodsSemantic web technology is used to design Disease Association Ontology (DAO-db) for representation and integration of disease associated information with diabetes as the case study. The functional associations of disease genes are integrated using RDF graphs of DAO-db. Three semantic web based scoring algorithms (PageRank, HITS (Hyperlink Induced Topic Search) and HITS with semantic weights) are used to score the gene nodes on the basis of their functional interactions in the graph.ResultsDisease Association Ontology for Diabetes (DAO-db) provides a standard ontology-driven platform for describing genes, proteins, pathways involved in diabetes and for integrating functional associations from various interaction levels (gene-disease, gene-pathway, gene-function, gene-cellular component and protein-protein interactions). An automatic instance loader module is also developed in present work that helps in adding instances to DAO-db on a large scale.ConclusionsOur ontology provides a framework for querying and analyzing the disease associated information in the form of RDF graphs. The above developed methodology is used to predict novel potential targets involved in diabetes disease from the long list of loose (statistically associated) gene-disease associations.     

Generating disease-pertinent treatment vocabularies from MEDLINE citations

ObjectiveHealthcare communities have identified a significant need for disease-specific information. Disease-specific ontologies are useful in assisting the retrieval of disease-relevant information from various sources. However, building these ontologies is labor intensive. Our goal is to develop a system for an automated generation of disease-pertinent concepts from a popular knowledge resource for the building of disease-specific ontologies.MethodsA pipeline system was developed with an initial focus of generating disease-specific treatment vocabularies. It was comprised of the components of disease-specific citation retrieval, predication extraction, treatment predication extraction, treatment concept extraction, and relevance ranking. A semantic schema was developed to support the extraction of treatment predications and concepts. Four ranking approaches (i.e., occurrence, interest, degree centrality, and weighted degree centrality) were proposed to measure the relevance of treatment concepts to the disease of interest. We measured the performance of four ranks in terms of the mean precision at the top 100 concepts with five diseases, as well as the precision-recall curves against two reference vocabularies. The performance of the system was also compared to two baseline approaches.ResultsThe pipeline system achieved a mean precision of 0.80 for the top 100 concepts with the ranking by interest. There were no significant different among the four ranks (p = 0.53). However, the pipeline-based system had significantly better performance than the two baselines.ConclusionsThe pipeline system can be useful for an automated generation of disease-relevant treatment concepts from the biomedical literature.     

An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management

ObjectiveChronic diseases are complex and persistent clinical conditions that require close collaboration among patients and health care providers in the implementation of long-term and integrated care programs. However, current solutions focus partially on intensive interventions at hospitals rather than on continuous and personalized chronic disease management. This study aims to fill this gap by providing computerized clinical decision support during follow-up assessments of chronically ill patients at home.MethodsWe proposed an ontology-based framework to integrate patient data, medical domain knowledge, and patient assessment criteria for chronic disease patient follow-up assessments. A clinical decision support system was developed to implement this framework for automatic selection and adaptation of standard assessment protocols to suit patient personal conditions. We evaluated our method in the case study of type 2 diabetic patient follow-up assessments.ResultsThe proposed framework was instantiated using real data from 115,477 follow-up assessment records of 36,162 type 2 diabetic patients. Standard evaluation criteria were automatically selected and adapted to the particularities of each patient. Assessment results were generated as a general typing of patient overall condition and detailed scoring for each criterion, providing important indicators to the case manager about possible inappropriate judgments, in addition to raising patient awareness of their disease control outcomes. Using historical data as the gold standard, our system achieved a rate of accuracy of 99.93% and completeness of 95.00%.ConclusionsThis study contributes to improving the accessibility, efficiency and quality of current patient follow-up services. It also provides a generic approach to knowledge sharing and reuse for patient-centered chronic disease management.     

Cautious observation or blanket scanning? An investigation into paediatric attendances to an emergency department after head injury

In September 2007, the National Institute for Health and Clinical Excellence (NICE) in the UK issued a newly updated guideline (CG56) on the early care of adults and children with head injuries.⁸ The guideline gives some new recommendations, in particular with regards to imaging of children with head injury.We undertook a study to investigate the management of children presenting with head injury to our emergency department and to assess their outcomes and the CT scanning rate. We then retrospectively applied the new NICE guidelines, using information documented in the case notes, to establish whether adherence to the guidelines would significantly affect CT scanning rates.237 paediatric head injury cases were seen over the 2-month period that was studied. The actual CT scanning rate observed was 2.1%, rising to 18.1% after strictly applying NICE criteria. This increased scanning rate raises some important issues with regards to patient safety and service provision.     

基于关联规则的名老中医冠心病用药规律研究

目的：分析、挖掘名医治疗冠心病心绞痛的用药规律,探索名医经验整理的方法。方法：收集7位名医治疗冠心病心绞痛典型医案115例,建立名医冠心病诊疗数据库,运用关联规则分析名医医治疗冠心病的方剂配伍规律。结果：在所使用的175种中药中,活血药、化痰药及补虚药之间的配伍是最常用的药物组合,瓜蒌薤白类方、活血通脉剂及生脉散等是名医治疗冠心病的基本方药,三者之间的配合应用构成了名医用药的一般规律。结论：以关联规则所得的名医治疗冠心病药对、药组反应了名医治疗冠心病标本同治的治疗思想,为以后的研究提供了方法学参考。     

数据挖掘在大型医疗设备故障诊断中的应用研究

本文阐述了数据挖掘的概念及实际应用的意义,尝试着将数据挖掘方法应用于我院部分大型医疗设备的故障监测与诊断中,取得了良好的收效。     

山东省肾综合征出血热疫源地演变规律

目的 探讨山东省肾综合征出血热(HFRS)疫源地时空动态轨迹,发现HFRS疫源地演变规律.方法 利用克力格模型和反向距离加权法模型,结合HFRS病例血清学分型资料及宿主动物监测及1974～2004年山东省HFRS疫情资料,估计HFRS发病水平,并用多重分型模型分析疫源地时空演变过程,确定其时空动态变化轨迹.结果 山东省HFRS疫源地性质的时空动态演化过程是单纯姬鼠型和单纯家鼠型疫源地独立存在→以姬鼠型和以家鼠型为主的混合型疫源地相互叠加共存→以家鼠型为主的混合型疫源地长期稳定期.结论 混合型HFRS疫源地并不是由姬鼠型自然疫源地演变而成的,而是姬鼠型和家鼠型HFRS疫源地的重叠或重合而成.     

数据挖掘中关联规则在睡眠对躯体疼痛影响评价中的应用

[目的]用关联规则挖掘睡眠与躯体疼痛的关系.[方法]采用数据挖掘技术中的关联规则找出睡眠相关指标与躯体疼痛之间的关联关系.[结果]主观睡眠计分、白天功能紊乱计分、睡眠潜伏期计分、习惯性睡眠效率计分、睡眠紊乱计分等高计分则会出现躯体疼痛,而低计分则不出现躯体疼痛等.[结论]关联规则方法用来评价睡眠对躯体疼痛的影响具有广阔的应用前景.