离群点检测算法在药品不良反应异常信号挖掘中的应用

将药品分类的前提下,以药品为对象,不良反应为属性,PRR值为参数,利用局部离群因子检测算法得到药品和其对应的离群因子。利用我国药品不良反应监测数据进行仿真实验,结果表明该离群点检测算法可以较好地用于药品不良反应异常信号的挖掘。     

TJ-IV型VCG微机自动分析系统的研制与临床应用

研制TJ-IV型心电向量图微机自动分析系统.与人工测算结果相比,本系统对正交心电图P波、Q波、R波、S波、R'波、S'波、T波起点和止点识别的准确性达98%,对各波谷点和峰点的识别准确性达100%.该系统开发诊断参数591项,包括46项创新指标.分析其中12项指标,人工测算结果与微机自动测算结果基本一致.该系统解决了心电向量图检测与分析中所存在的若干技术难题,提高了本系统抗干扰性能和安全性能,图形自动识别准确率高,测算参数精确度高,检测速度快.     

基于CLSI EP9-A3的血清人绒毛膜促性腺激素β亚单位方法学比对及偏移评估

目的 分析不同检测系统间血清人绒毛膜促性腺激素β亚单位(β-HCG)检测结果的可比性。方法 分别用贝克曼DXI 800检测系统(参比系统x)与星童Pylon 3D检测系统(待测系统y)测定β-HCG。参照美国临床和实验室标准化协会(CLSI) EP9-A3文件进行方法学比对和偏移评估,通过广义极端学生化偏差(ESD)法检验离群值点,选用最佳回归模型拟合回归方程,并计算医学决定水平处的偏移,以1/2澳大利亚室间质评的评价标准作为可接受标准。结果 广义ESD法检验发现1个离群值点,剔除再补充1个数据后,无离群值。通过Passing-Bablok回归分析得回归方程:y=-2. 000+1. 000x。根据Passing-Bablok回归方程估算β-HCG在各医学决定水平处(5 IU/L、25 IU/L、500 IU/L、1 000 IU/L)的偏移结果,偏移均小于可接受标准。结论 星童Pylon 3D检测系统与贝克曼DXI 800检测系统之间血清总β-HCG的检测结果具有可比性。     

广义可加模型稳健估计在空气污染对健康影响评价中的应用

目的针对大气污染与健康关系研究中拟合广义可加模型时的离群点进行诊断并试图减小其影响。方法将经典的稳健M估计方法引入广义可加模型。结果模拟及实例分析可见稳健估计的结果较好。结论在离群点存在时对广义可加模型进行稳健估计是必要的。     

TJ-Ⅳ型VCG微机自动分析系统的研制与临床应用

研制TJ-Ⅳ型心电向量图微机自动分析系统。与人工测算结果相比,本系统对正交心电图P波、Q波、R波、S波、R'波、S'波、T波起点和止点识别的准确性达98％,对各波谷点和峰点的识别准确性达100％。该系统开发诊断参数591项,包括46项创新指标。分析其中12项指标.人工测算结果与微机自动测算结果基本一致。该系统解决了心电向量图检测与分析中所存在的若干技术难题.提高了本系统抗干扰性能和安全性能,图形自动识别准确率高,测算参数精确度高,检测速度快。     

logistic回归中高杠杆点的检测

本文主要研究了ｌｏｇｉｓｔｉｃ回归中高杠杆点的检测方法，指出了传统用ｈｉｉ检测高杠杆点的不合理性，建议用ｈ＊ｉ＝ｈｉ／（ｐｉ（１－ｐｉ））作为检测高杠杆点的统计量，并在此基础上提出｛（ｈ＊ｉ，ｅｐｉ）｝图用于检测高杠杆点和异常点。本文还通过Ｍｏｎｔｅ－Ｃａｒｌｏ方法说明了｛（ｈ＊ｉ，ｅｐｉ）｝图不但能检测出高杠杆点和异常点，还可区分高杠杆点的“有害”与“无害”     

Ⅱ型糖尿病疾病相关细菌的筛选探究

目的研究空腹血糖受损(IFG)患者和Ⅱ型糖尿病(T2DM)患者肠道菌群整体结构多样性,筛选出可能与T2DM相关的细菌。方法利用末端限制性片段长度多态性(T-RFLP)技术检测正常人群、IFG患者和T2DM患者肠道菌群整体结构;运用多元统计方法偏最小二乘判别分析(PLS-DA)对T-RFLP数据进行分析,筛选出与T2DM可能相关的细菌。结果 T2DM组肠道菌群整体结构的个体差异较对照组和IFG组增大,初步筛选出3种可能与Ⅱ型糖尿病相关的细菌,Bacillus sp.ES-SL-2、Fusobacterium nucleatum和Thermosyntropha sp.L-60。结论本研究初步筛选出了可能与Ⅱ型糖尿病相关的肠道菌株,证实T-RFLP联合PLS-DA应用于Ⅱ型糖尿病早期发现和疾病状态监控的可行性。     

强直性脊柱炎FcRL基因与环境暴露因素交互作用研究

目的 研究强直性脊柱炎(AS)中环境因素与FcRL基因的交互作用.方法 对169例AS患者环境因素进行调查,PCR-SSP技术检测HLA-B27亚型,连接酶检测反应方法检测FcRL3、FcRL5基因SNP位点,采用单纯病例研究方法就AS基因与环境交互作用进行研究.结果 本次研究中169例AS病人HLA-B27共检测出B * 2704、B*2705、B* 2710、B*2702和B*2715 五个亚型;FcRL3基因中rs7522061位点与饮食偏咸存在交互作用,与其他环境因素无交互作用;FcRL5基因中rs12036228位点只与饮食偏咸因素存在交互作用;FcRL5基因rs6427384位点分别与饮酒和饮食偏咸有交互作用.结论 饮食偏咸可能作为环境风险因素与易感基因FcRL5共同作用增加AS的发病风险;适度饮酒可能减低FcRL5基因(rs6427384)诱发AS的危险性;FcRL3基因(rs7522061)在存在饮食偏咸因素时起到效应修饰作用,增加疾病风险.     

景洪市2004～2010年CDC实验室质量控制考核结果分析

目的 验证实验室检测能力、检测人员的操作技术水平和仪器设备的运行状态,进一步完善实验室质量管理体系.方法 使用《生活饮用水卫生规范》卫生部(2001年版)、《生活饮用水卫生标准检验方法》卫生部GB/T5750-2006规定的方法对未知合成水样铁、锰、砷、铜、六价铬、硫酸盐、氯化物、总硬度、硝酸盐氮、氟化物、氨氮进行检测分析.结果 2004～2010年景洪市疾控中心实验室质量控制考核,共11个项目27次,合格10个项目26次,合格率96.3％.结论 通过实验室质量控制考核,有助于发现实验室检测分析中存在的问题及检测结果的不合格项,从而找出检测结果离群的各种原因,依照能力验证纠正措施,有效地进行整改.     

线性回归分析中异常点的诊断统计量

在线性回归分析中，可将观察资料中出现的离群点分为高杠杆点和异常点。t分布化普通残差及t分布化预报残差对捡出资料中的异常点部不理想。作者提出用残差平方和减少量及由此导出的偏F检验作为检验异常点的统计量。这一统计量也适用于诊断非线性回归中的资料异常点。文中用实例对这一统计量作了详细说明。     

Stata软件在Meta分析中异质性检验的应用

介绍Stata软件在Meta分析异质性检验中的应用.将研究实例建立数据集,采用Stata9软件中相应命令语句实现固定效应模型森林图法附带的Q统计量和统计量Ⅰ2.检验法、H值法、Galbraith图法.采用Q检验、H统计量检验出研究间是否存在异质性,Ⅰ2统计量确定异质性程度,Galbraith图可看出哪些研究是异质性来源的异常点.结论 :Stata软件中四种方法可简单、快速地实现Meta分析中异质性检验.其中H和,Ⅰ2统计量异质性检验结果较为稳健,Galbraith图可直观地显示出研究结果异质性的异常点.     

Exploring heterogeneity in meta-analysis: is the L'Abbé plot useful?

By using a published meta-analysis as an example, this paper discusses the use of L'Abbe plot for investigating the potential sources of heterogeneity in meta-analysis. As compared with other graphic procedures, the L'Abbe plot is useful to identify not only the studies having different results from other studies, but also the study arms that are responsible for such differences. This may be important for determining the focus of heterogeneity investigations. Results of stochastic simulation indicate that, purely because of random variation, studies with event rates of around 50% are more likely to be identified as outliers in a L'Abbe plot. This paper also demonstrates that different methods may identify different trials as "outliers" in meta-analysis.     

An Assessment of Mercury Loading in Core Sediments of Sunderban Mangrove Wetland,India (A Preliminary Report)

This is a preliminary report on total mercury (T(Hg)) in core sediments (<63 microm particle size) of Sunderban mangrove wetland, northeastern part of the Bay of Bengal, India. Cold vapor atomic absorption spectrometry (CVAAS) was used for T(Hg) determination. The concentration varies from 9.8 to 535.1 ppb (ngg(-1)). Results revealed variations over premonsoon and postmonsoon month at different core depth, as well as in studied three sampling stations, located at the site of three rivers: Hugli River (S(1)), Matla River (S(2)) and Bidyadhari River (S(3)). Elevated concentration of T(Hg) in subsurface layer (4-8 cm) of the core at S(2) is attributed to remobilization of mercury from deeper sediment (32-36 cm). Positive correlation is present between total Hg and clay content. Based on index of geoaccumulation (I(geo)) and Effects-Range Low (ER-L) value (150 ppb) it is considered that the sediments are till now unpolluted. As a consequence, there is less chance of ecotoxicological risk to organisms living in studied sediments. Two statistical methods were applied to determine T(Hg) anomalies. Box plot method showed one extreme and three outliers in S(1) at postmonsoon season. Two extremes were found at S(2) at 4-8 and at 32-36 cm in premonsoon period. In S(3) there was no anomaly by box plot method. MAD method was more sensitive than box plot method and T(Hg) anomaly was detected at 12-16 cm in S(3) during postmonsoon season. The data reported are useful baselines for T(Hg) in Sunderban mangrove wetland, India and would be of help in future sediment quality studies.     

Multivariate outlier detection in medicare claims payments applying probabilistic programming methods

The rising elderly population continues to demand more cost-effective healthcare programs. In particular, Medicare is a vital program serving the needs of the elderly in the United States. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for, and risk of, fraudulent activities. Out of the many possible factors for the rising cost of healthcare, fraud is a major contributor, but its impacts can be lessened through the use of fraud detection methods. In this paper, we assess possible illegitimate activities by looking at the amounts paid to providers for services rendered to patients. We propose a novel method for fraud detection that focuses on discovering outliers in Medicare payment data using multiple predictors as model inputs. Our multivariate outlier detection approach is twofold: (1) create a Multivariate Adaptive Regression Splines model to produce studentized residuals and, (2) use the residuals as input into a general univariate outlier detection model, based on full Bayesian inference, using probabilistic programming. Using this approach, we are able to incorporate multiple variables to detect outliers with a model that provides probability distributions, with credible intervals, rather than just point values, as with most common outlier detection methods. Additionally, these credible intervals further enhance confidence that the detected outliers should in fact be considered outlying values, thus possibly fraudulent activities. Our results show that the successful detection of these possibly fraudulent activities can provide effective and meaningful results for further investigation within various medical specialties.     

Development of a methodology for the detection of hospital financial outliers using information systems

Comparison of financial indices helps to illustrate differences in operations and efficiency among similar hospitals. Outlier data tend to influence statistical indices, and so detection of outliers is desirable. Development of a methodology for financial outlier detection using information systems will help to reduce the time and effort required, eliminate the subjective elements in detection of outlier data, and improve the efficiency and quality of analysis. The purpose of this research was to develop such a methodology. Financial outliers were defined based on a case model. An outlier‐detection method using the distances between cases in multi‐dimensional space is proposed. Experiments using three diagnosis groups indicated successful detection of cases for which the profitability and income structure differed from other cases. Therefore, the method proposed here can be used to detect outliers. Copyright © 2013 John Wiley & Sons, Ltd.     

A new approach to outliers in meta-analysis

The synthesis of evidence from trials and medical studies using meta-analysis is essential for Evidence Based Medicine. However, problematical outlying results often occur even under the random-effects model. We propose a model that allows a long-tailed distribution for the random effect, which removes the necessity for an arbitrary decision to include or exclude outliers. In this approach, they are included, but with a reduced weight. We also introduce a modification of the forest plot to show the downweighting of outliers. We illustrate the methodology and its usefulness by carrying out both frequentist and Bayesian meta-analyses using data sets from the Cochrane Collaboration.     

主成分回归中异常点的稳健诊断

目的探讨主成分回归模型中存在异常点时的稳健诊断方法。方法采用基于MVT和LMS方法的一种稳健主成分回归方法来诊断异常点，并结合实例进行方法的对比。结果与未去除异常点时得到的回归方程相比较，具有较小的残差平方和，拟合效果较好。解决了主成分回归中存在异常点的问题。结论当主成分回归中存在异常点时，本文中所述的稳健诊断方法具有较高的稳健性，在主成分回归分析中有较好的应用前景。     

Defining exposure in case-control studies: a new approach

Many epidemiologists conducting case-control studies choose to dichotomize their exposure data to make the analysis of the data easier and its presentation more straightforward. The choice of a specific rule for dichotomization can have a large effect on the outcome measure, the odds ratio, although this effect is rarely studied. The authors present a graphic approach for exploring this effect, the quantile-quantile (Q-Q) plot. By examining a Q-Q plot, an investigator simultaneously gains information about the distribution of exposures among cases, the distribution of exposures among controls, and odds ratios at all possible cutpoints and their standard errors. In addition, by finding the slope at each point along the Q-Q curve, it is possible to estimate the rate ratios for each possible value of exposure. The authors view the use of the Q-Q plot as an exploratory tool. It enables the investigator to become more familiar with the data and check for irregularities such as outliers, nonlinearities, or nonmonotonic dose-response curves, and idiosyncratic variations of the odds ratio. The authors present an example evaluating the risk of low birth weight as a function of mother's age for Boston births in 1984.     

基于ARIMA模型对传染病监测数据异常点的探测研究

目的 介绍基于ARIMA模型的异常点探测方法,研究传染病监测数据中各类异常点的特征,针对异常点诊断结果,作出合理解释,提出处理方法。方法 收集公共卫生科学数据中心提供的2006 - 2012年某省淋病发病率数据,利用SAS程序中的ARIMA模块探测各类异常点,通过文献查阅,专家咨询等方法分析出现原因,提出基本的处理方法。结果 异常点诊断结果显示2007年1月、2010年3月和2010年4月所对应的观察值为加性异常点。2006年5月、2009年9月和2010年10月对应的观测值属于水平漂移异常点。结论 2006 - 2012年某省淋病发病率中不仅含有加性异常点还有水平漂移异常点。就加性异常点而言,反查数据后发现,这类异常点的出现可能是由于数据观测与收集误差引起的常规的离群点;而对水平漂移异常点,通过文献查阅、专家咨询等途径发现是由于数据受到新机制的影响,如疾病的暴发流行、新的卫生政策的出台、环境改变引起高危行为的增加等原因。