HTML数据内容的抽取与集成

在XML基础上，利用HTML Tidy可实现轻量级的Web数据挖掘和转换。转换过程主要解决的是HTML文档及其集合要表达的模式信息的分离。转换步骤是利用HTML Tidy提供的标准类库，净化HTML文档，借助DOM生成树对HTML元素结构做进一步分析，最后通过XSL、XPATH等自动提取转换。     

基于数据安全的健康医疗科学数据分级指南研究

目的/意义完善健康医疗科学数据分级保护,促进数据安全共享、充分利用,提升数据资源价值。方法/过程采用文献分析法,并开展多轮专题专家小组讨论,研讨健康医疗数据分级的原则、规则和实施步骤。结果/结论基于健康医疗科学数据安全相关法律法规或政策要求,构建健康医疗科学数据分级指南,提出健康医疗科学数据分级的具体原则、评级规则与实施步骤,为健康医疗科学数据生产、管理及应用活动中的数据分级提供参考。     

Using Citation Data to Improve Retrieval from MEDLINE

ObjectiveTo determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant.Design and MeasurementsA direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology.ResultsCitation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms.ConclusionAlgorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.     

以数据为主线探讨区域卫生信息平台建设

结合重庆市区域卫生信息化建设实践,从数据标准、数据质量管控、数据应用支持和数据应用角度分析区域卫生信息平台建设中的影响因素,提出相应解决方案,即平台规划设计应具有前瞻性、推进精细化管理机制.     

开放数据视角下健康医疗数据价值评估指标体系研究

介绍数据价值评估相关研究进展,通过德尔菲咨询法、层次分析法等建立健康医疗数据价值评估指标体系,阐述该指标体系设计方法和步骤、应用和意义以及研究方法科学性,为识别和评估健康医疗数据价值奠定基础,有助于推动高价值医疗数据开放进程。     

健康管理与深度测序数据解析的挑战

介绍深度测序技术对生物医学研究和社会的影响,讨论深度测序数据解析及其在健康管理应用中所面临的挑战和机遇,包括数据存取、计算技术、数据应用、人才缺失与跨学科人才教育等方面。     

Data and models determine treatment proposals--an illustration from meta-analysis

A relevant problem in meta-analysis concerns the possible heterogeneity between trial results. If a test of heterogeneity is not significant the trials are often considered to be "homogeneous" and the individual trial results are replaced by an overall mean effect size and its confidence interval ("equal effects model"). If the trials are heterogeneous the individual trial effect sizes are conserved ("fixed effects model"). In a more flexible approach ("random effects model"), each trial makes use of knowledge from the other trials so individual effect sizes are "shrunken" towards an overall mean effect size. The more flexible tool may be useful for doctors involved in a trial when the outcome of their individual trial differs markedly from the overall mean effect size. Where a particular trial result is opposite in direction to the overall mean result, a conflict may arise: should a new patient be treated with the new method or not? The more flexible position and a graphical comparison of the three approaches are likely to be helpful in guiding the decision. Applying different models to the same data may lead to apparently paradoxical results: an individual trial result may be interpreted to be beneficial or harmful depending on the choice of model.     

准确的病案数据在医院中的重要作用和意义

目的本文着重分析医院病案数据的重要性:病案数据功能的拓展,在医学科研中的作用,在疾病预防和统计中的应用,在医院管理中的作用,如医院发展分析、医院医疗技术水平分析、医院各科考核排名分析、医院成本核算、医院费用结算、医疗付款方式、单病种监控、医院营销等方面的分析、引出优质存储病案的必要.方法通过对比分析电子病案和纸质病案数据,存储和流通方式现状,寻找出纸质病案和电子病案内容不一致、归档病案数据缺陷、不完整、错误的原因和危害.结果规范病案数据收集保证医疗统计数据准确,主要在于加强电子病案和纸质病案的管理,分析内容不一致、归档病案数据缺陷、不完整、错误的成因和对策,做好病案质量审核,强化发展和应用电子病案信息系统.结论转变观念,加强纸质病案和电子病案的同期管理,开发利用病案统计信息,强化病案管理中的法律意识,使电子病案与纸质病案具有同等的法律效力.     

轻度认知障碍患者的双向转归研究

背景轻度认知障碍（MCI）患者的认知轨迹高度可变。每年有10%~15%的MCI患者进展为痴呆,近24%逆转为认知正常（NC）。有关MCI患者的双向转归,尤其是MCI逆转为NC的研究开展较少。目的构建MCI患者不同转归的多分类模型,寻找MCI患者双向转归（逆转、稳定、进展）的影响因素。方法本研究数据来源于美国国家阿尔茨海默病症协调中心统一数据集（NACC UDS）,筛选2005—2019年初次诊断为MCI并至少随访2次且数据无缺失的397例患者,采集其基本信息、身体检查指标、既往史及相关评估量表得分。基于Boruta算法进行特征选择,采用随机森林进行MCI不同转归的多分类辅助诊断;采用多分类逐步Logistic回归分析探究MCI患者不同转归的影响因素。结果纳入的397例MCI患者中,124例逆转为NC,逆转率为31.23%;177例稳定,稳定率为44.58%;96例进展为痴呆,进展率为24.18%。将397例MCI患者的不同转归进行分层三分类,总准确度（ACC）为67.58%。在多个二分类中,进展和逆转、逆转和稳定的分类性能较优,ACC均在90%以上;分层三分类中稳定→逆转→进展的总ACC为84.38%。多分类逐步Logistic回归分析结果显示,对于MCI患者进展为痴呆而言,年龄≥80岁〔OR=0.260,95%CI（0.117,0.574）〕、自我报告认知障碍〔OR=0.295,95%CI（0.139,0.623）〕、临床痴呆评定量表（CDR）得分降低〔OR=0.220,95%CI（0.092,0.525）〕为保护因素,既往有卒中〔OR=2.896,95%CI（1.370,6.122）〕、既往有大便失禁〔OR=6.556,95%CI（1.787,24.047）〕、功能活动问卷（FAQ）得分升高〔OR=1.048,95%CI（1.003,1.095）〕为危险因素。对于MCI患者逆转为正常而言,年龄≥80岁〔OR=0.289,95%CI（0.091,0.914）〕、肥胖〔OR=0.236,95%CI（0.075,0.740）〕、自我报告认知障碍〔OR=0.289,95%CI（0.111,0.757）〕、CDR得分升高〔OR=0.015,95%CI（0.003,0.089）〕为危险因素,简易精神状态量表（MMSE）得分升高〔OR=1.708,95%CI（1.428,2.043）〕和动物命名正确数升高〔OR=1.139,95%CI（1.046,1.240）〕为保护因素。结论MCI患者的转归受多个因素影响,高龄、肥胖、既往发生过卒中、既往发生过大便失禁、自我报告认知障碍和日常功能障碍是MCI双向转归的重要影响因素。     

Human Activity Recognition from Body Sensor Data using Deep Learning

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.     

中医病案数据挖掘现状分析

从工具选择、数据采集、数据预处理、数据挖掘、结果翻译5个方面对中医病案数据挖掘研究现状进行概述,发现中医病案数据挖掘的信息主要包括症状、证型、方剂等,涉及的挖掘方法有关联规则、复杂网络、聚类、分类等。本研究可为中医临床数据挖掘选题及方法选择提供参考。     

An XML-based System for Synthesis of Data from Disparate Databases

Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype–phenotype correlation analyses. This application implements a method of phenotype–genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype–genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddison's concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.     

Data from May 14 census will play valuable health care role

May 14 is Census Day in Canada. Information gathered from this survey, conducted every 5 years, is important to organizations like the CMA. They can use the data to estimate physician-resource requirements and medical services needed by a changing population.     

由两元数据预计三元气液平衡

着重介绍Wilson两参数方程,如何从两元汽液平衡数据来求得两两相互作用的两参数,进而求解三元气液相平衡的计算法.推荐实用的计算两个参数的FORTRAN程序,并举例说明其应用.     

2010-2011年医院感染现患率调查分析

目的评价医院感染现患率调查结果的变化趋势,制定预防控制医院感染的措施,从而有效预防与控制医院感染。方法按照四川省医院感染监测网制定的全省医院感染现患率调查方案,于2010、2011年的10月某日对该院住院患者进行医院感染现患率调查,并对2010、2011年医院感染现患率调查结果进行比较分析。结果2011年医院感染现患率为3．5％,与2010年的现患率2．8％比较差异无统计学意义。医院感染部位以下呼吸道居首位,占60．0％,其次为手术部位,占20．0％,两次调查医院感染现患率部位构成比,差异无统计学意义。抗菌药物使用,一联用药由84．2％下降至80．3％,差异无统计学意义,二联用药由14．0％升高到79．7％,差异有统计学意义（P〈0．01）;医院感染病原菌2011、2010年检出的前三位的致病菌均为金黄色葡萄球菌、肺炎克雷伯菌、鲍曼不动杆菌,差异无统计学意义。结论根据调查结果有针对性地开展医院感染目标性监测,采取有效的预防控制措施,可不断降低医院感染的发生。     

左归丸“阳中求阴”之数据挖掘

目的:通过对左归丸补阴补阳实际功效值的计算,探讨左归丸补阴之功用,为其"阳中求阴"的论点提供数据支持。方法:将左归丸中各药物名称及功效标准化,然后进行左归丸补阴、补阳实际功效值的计算,用数学原理对左归丸补阴、补阳的功效强度进行解析。结果:左归丸补阴的实际功效值为6.07,补阳的实际功效值为1.44。该方补阴补阳实际功效值的模式:大量补阴的功效+少量补阳功效的组合。结论:左归丸补阴的功效值远远大于其补阳的功效值,说明左归丸的主要功用倾向于补阴。在一定程度上证实了左归丸"阳中求阴"的论点:从阳引阴,以动促静,在阳药的带动下,以期能更有效地发挥阴药的作用。