Observed communication between oncologists and patients:A causal model of communication competence

AIM: To investigate and test a causal model derived from previous meta-analytic data of health provider behaviors and patient satisfaction.METHODS: A literature search was conducted for relevant manuscripts that met the following criteria: Reported an analysis of provider-patient interaction in the context of an oncology interview; the study had to measure at least two of the variables of interest to the model(provider activity,provider patient-centered communication,provider facilitative communication,patient activity,patient involvement,and patient satisfaction or reduced anxiety); and the information had to be reported in a manner that permitted the calculation of a zero-order correlation between at least two of the variables under consideration.Data were transformed into correlation coefficients and compiled to produce the correlation matrix used for data analysis.The test of the causal model is a comparison of the expected correlation matrix generated using an Ordinary Least Squares method of estimation.The expected matrix iscompared to the actual matrix of zero order correlation coefficients.A model is considered a possible fit if the level of deviation is less than expected due to random sampling error as measured by a chi-square statistic.The significance of the path coefficients was tested using a z test.Lastly,the Sobel test provides a test of the level of mediation provided by a variable and provides an estimate of the level of mediation for each connection.Such a test is warranted in models with multiple paths.RESULTS: A test of the original model indicated a lack of fit with the summary data.The largest discrepancy in the model was between the patient satisfaction and the provider patient-centered utterances.The observed correlation was far larger than expected given a mediated relationship.The test of a modified model was undertaken to determine possible fit.The corrected model provides a fit to within tolerance as evaluated by the test statistic,χ2(8,average n = 342) = 10.22.Each of the path coefficients for the model reveals that each one can be considered significant,P < 0.05.The Sobel test examining the impact of the mediating variables demonstrated that patient involvement is a significant mediator in the model,Sobel statistic = 3.56,P < 0.05.Patient active was also demonstrated to be a significant mediator in the model,Sobel statistic = 4.21,P < 0.05.The statistics indicate that patient behavior mediates the relationship between provider behavior and patient satisfaction with the interaction.CONCLUSION: The results demonstrate empirical support for the importance of patient-centered care and satisfy the need for empirical casual support of provider-patient behaviors on health outcomes.

ICA Based Identification of Time-Varying Linear Causal Model

Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.

Application of causal model to maternal smoking cessation intervention in pregnancy

The adverse effects of maternal smoking during pregnancy on both the offspring and women are well known. The main objective of this research article is to provide health professional causal modelling approach to make a more comprehensive assessment of major determinants of smoking behaviour during and after pregnancy and consequently the outcomes of pregnant women smoking which are adversely affecting both the offspring and pregnant women. The causal model based on theory and evidence was modified and applied to material smoking cessation intervention to control the adverse effects of smoking on offspring obesity and neurodevelopment. In this approach a generic model links behavioural determinants, causally through behaviour, to physiological and biochemical variables, and health outcomes. It is tailored to context, target population, behaviours and health outcomes. The model provides a rational guide to appropriate measures, intervention points and intervention techniques, and can be tested quantitatively. The causal modelling approach showed promising results which can be used to help maternal smoking women to understand the risk of smoking and help them to quit smoking. The regression analysis of maternal smoking women BMI (n = 1000) on offspring BMI was statistically significant, p 0.05). This supported the hypothesis that maternal smoking women BMI during pregnancy is an important determinant of offspring obesity and consequently the risk factors of cardiovascular development. The causal modelling approach is unique as it provides an incentive to health professional to use these models to target any important and modifiable determinants of the maternal smoking behaviour and decrease the risk of adverse pregnancy outcomes for the offspring and the mother.

Robust Parameter Identification Method of Adhesion Model for Heavy Haul Trains

A robust parameter identification method based on Kiencke model was proposed to solve the problem of the parameter identification accuracy being affected by the rail environment change and noise interference for heavy-duty trains. Firstly, a Kiencke stick-creep identification model was constructed, and the parameter identification task was transformed into a quadratic programming problem. Secondly, an iterative algorithm was constructed to solve the problem, into which a time-varying forgetting factor was added to track the change of the rail environment, and to solve the uncertainty problem of the wheel-rail environment. The Granger causality test was adopted to detect the interference, and then the weights of the current data were redistributed to solve the problem of noise interference in parameter identification. Finally, simulations were carried out and the results showed that the proposed method could track the change of the track environment in time, reduce the noise interference in the identification process, and effectively identify the adhesion performance parameters.

Causal inference using regression-based statistical control: Confusion in Econometrics

Regression is a widely used econometric tool in research. In observational studies, based on a number of assumptions, regression-based statistical control methods attempt to analyze the causation between treatment and outcome by adding control variables. However, this approach may not produce reliable estimates of causal effects. In addition to the shortcomings of the method, this lack of confidence is mainly related to ambiguous formulations in econometrics, such as the definition of selection bias, selection of core control variables, and method of testing for robustness. Within the framework of the causal models, we clarify the assumption of causal inference using regression-based statistical controls, as described in econometrics, and discuss how to select core control variables to satisfy this assumption and conduct robustness tests for regression estimates.

Using granger-geweke causality model to evaluate the effective connectivity of primary motor cortex, supplementary motor area and cerebellum

Currently, Granger-Geweke causality models have been widely applied to investigate the dynamic direction relationships among brain regions. In a previous study, we have found that the right hand finger-tapping task can produce relatively reliable brain response. As an extension of our previous study, we developed an algorithm based on the classical Granger- Geweke causality model to further investigate the effective connectivity of three brain regions (left primary motor cortex (M1), supplementary motor area (SMA) and right cerebellum) that showed the most robust brain activations. Our computational results not only confirm the strong linear feedback among SMA, M1 and right cerebellum, but also demonstrate that M1 is the hub of these three regions indicated by the anatomy research. Moreover, the model predicts the high intermediate node density existing in the area between SMA and M1, which will stimulate the imaging experimentalists to carry out new experiments to validate this postulation.

基于非稳态加性噪声模型的因果发现算法

因果发现旨在通过观测数据挖掘变量间的因果关系。现有的因果发现方法大多假定数据的产生过程是平稳的,然而在实际环境下往往不满足稳态假设,导致结果不可靠。研究发现,在一些场景中的非稳态扰动与时序信息高度相关。因此,在加性噪声模型基础上将非稳态扰动刻画为一项关于时序信息的函数,设计非稳态加性噪声模型,并给出非稳态加性噪声模型的识别条件,提出一种两阶段的因果关系学习算法。第1阶段利用回归计算得到变量残差,再检验残差与回归特征集的独立性从而选出叶子节点,迭代得到观测变量集的因果次序;第2阶段再次进行回归计算和独立性检验,消除第1阶段中冗余的因果关系,从而得到观测变量集的因果结构。实验结果表明,与基于约束的异构/非平稳因果发现、LPCMCI和Ti MINo算法相比,该算法在仿真数据集上取得了最优的效果,平均F1值达到0.85;而在真实因果结构数据集中,该算法的F1值平均提升41.12%,能够从非稳态数据集中恢复出更多因果结构的信息。

基于因果正则化极限学习机的风电功率短期预测方法

随着风电并网比例的逐年提高,电力系统对风电功率预测的准确性和稳定性提出了更高要求。对于同一风电场而言,为了避免不同特征选择方法所选择的风电场特征子集不同,从因果关系的角度出发,提出了一种基于因果正则化极限学习机(causal regularized extreme learning machine, CRELM)的风电功率短期预测方法。首先将极限学习机(extreme learning machine, ELM)建模为结构因果模型(structural causal model, SCM),在此基础上计算隐藏层神经元与输出层神经元之间的平均因果效应向量。然后将该平均因果效应向量与输出层权重相结合构成因果正则化项,在最小化训练误差的同时最大化网络的因果关系,以进一步提升模型的预测准确性和预测稳定性。最后,以国内蒙西某风电场数据为例,与采用特征选择或不采用特征选择的预测模型相对比,验证了所提方法的有效性和适用性。

郑州“7·20”地铁水淹事件STAMP致因分析

为探讨河南郑州“7·20”地铁5号线伤亡事件致因,综合运用系统理论事件模型与过程(STAMP)模型,分析5号线伤亡事件安全控制结构,据此逐级辨识事件致因;基于复杂网络理论将事件致因及其联系抽象为网络的节点和边,建立地铁水灾事件网络;计算聚类系数、度数等拓扑参数定量分析事件致因性质,确定关键致因。结果表明:地方党委政府面对汛情的应对部署不紧不实,缺少有效的组织动员和有关部门对工程建设存在失管失察是事件的深层根源;建设单位、设计单位等五方主体单位及运维单位的主体责任失职是事件的主观关键致因;应从政府及有关部门、五方主体单位和运维单位方面采取多元协同的风险防控措施。

交通安全意识对非机动车骑行者危险骑行行为的影响研究

安全意识在促进安全行为方面发挥着重要作用,但由于安全意识具有多维性和复杂性,难以直接测量。为探究交通安全意识对危险骑行行为的影响,通过云模型选取安全态度、危险认知、安全素质和外界环境这4个潜变量,作为影响交通安全意识的结构要素,并基于调查问卷数据开展实证研究。运用Mplus 8.0软件构建“交通安全意识-危险骑行行为”结构方程模型,量化交通安全意识各要素作用于危险骑行行为的因果链路。采用Bootstrap法检验安全素质、危险认知和安全态度的中介作用,梳理外界环境对危险骑行行为的直接和间接关系;再利用分层回归模型,验证交通安全知识在交通安全意识与危险骑行行为间的调节效应。研究结果表明:①结构方程模型拟合良好,交通安全意识的4个要素分别与危险骑行行为呈显著的负相关,其中,危险认知对无意行为的影响最大(-0.331),安全态度对有意行为的影响最大(-0.332);②中介效应显示外界环境作为外生变量可直接作用于行为,也可通过安全素质、危险认知和安全态度对骑行者的行为产生影响;③交通安全知识的调节作用显著(ΔR^(2)=0.017,P<0.05),该变量强化了交通安全意识与危险骑行行为的负向影响关系,其简单斜率关系表明,当骑行者交通安全知识水平较高时,交通安全意识对危险骑行行为的作用效果更强。

信息计量学的创新之道:近年优秀国际期刊论文的启示

[目的/意义]在智联网时代,信息计量学的研究环境、研究对象、研究内容和研究方法都发生了深刻变化。揭示近年信息计量学研究的创新之道,有助于了解信息计量学领域的前沿知识,为信息计量学的高质量发展提供参考。[方法/过程]文章系统梳理了2019—2022年的国际信息计量学研究,归纳出三类创新之道。[结果/结论]一是通过引入计算机科学、数学、经济学和社会学等领域的先进知识,实现交叉融合创新;二是辩证思考经典的计量方法、计量指标和计量模型,实现分析方法创新;三是顺应智能预测和因果推断的时代潮流,实现研究范式创新。结论表明,为适应新时代的发展需要,信息计量学研究者必须积极汲取多学科知识,拓展分析方法的适用范围,不断突破面向国家需求的战略问题。

我国牛肉价格波动影响因素研究——基于VAR模型的实证分析

为保证牛肉价格合理稳定、推动牛肉市场持续健康发展,文章基于2011年12月—2022年12月中国畜牧业月度数据,以内部传导和外部冲击为视角,选取玉米价格、生产者预期、犊牛价格、国家政策构建VAR模型,实证检验我国牛肉价格波动影响因素及程度。结果显示,玉米价格、生产者预期、犊牛价格、国家政策均可对牛肉价格造成影响。其中,玉米价格可通过内部传导机制对我国牛肉价格波动产生显著影响,且稳定贡献率在8%左右;国家政策可通过外部冲击机制对我国牛肉价格波动形成显著影响,贡献率为15%;前期牛肉价格可显著影响后期牛肉市场价格。因此,文章提出降低肉牛产业饲料成本、加大肉牛产业政府财政支持力度、完善牛肉价格波动市场监管体系的建议,以期为稳定牛肉价格、促进畜牧产业可持续发展提供参考。

基于因果自回归流模型的因果结构学习算法

因果自回归流模型已经在非独立噪声等场景的因果方向推断问题上取得了一定的进展,但在多个结点的场景下仍存在全局结构搜索带来的准确度低和计算时间复杂度高的问题。面向非时序观察数据设计一种两阶段因果结构学习算法。在第一阶段,基于观测数据的条件独立性,对完全无向图通过条件独立性检验得到基本的因果骨架;在第二阶段,基于因果自回归流模型,通过标准化流的方法计算骨架中每条无向边在不同方向上的边缘似然概率,进而通过比较边缘似然概率进行因果方向推断。实验结果表明:该算法在多组不同参数生成的仿真因果结构数据集上均有较好的表现,与现有的主流因果结构学习算法相比,F1值平均提升15%~28%;在真实因果结构数据集实验中,该算法能够较为完整准确地学习到变量间的因果关系,与主流的因果结构学习算法相比,F1值平均提升28%~48%,具有更强的鲁棒性。

基于因果熵的无人集群对抗评估指标分配方法

针对传统无人集群对抗评估指标分配方法仅能选出相关性较好的评估指标,存在指标混淆和虚假相关问题,提出因果熵概念,用于度量评估指标和对抗任务完成效果间的确定性程度,从而滤除评估指标之间的混淆效应。以仿真环境下的无人集群空地协同围捕试验为例,通过评估指标因果分配和约简优选出更能反映无人集群对抗能力且相对独立的评估指标。仿真结果表明,所提方法可以有效地优化评估指标体系,获取更具代表性的评估指标,具有一定的普适性。

基于Modelica语言的电液伺服阀非因果建模仿真

针对电液伺服阀这一典型机械、电子、磁场、液压与控制耦合的复杂系统,考虑其多领域、多层次化的特点,应用多领域建模仿真语言Modelica对电液伺服阀进行建模仿真。不同于传统的电液伺服阀控制框图建模分析方法,Modelica语言的非因果性与面向对象性,使得该方法具有强可读性,强可用性,便于修改等特点,克服了传统方法不能从底层元件反映伺服阀特性、考虑因果性、不易修改与重用的缺点。仿真结果显示,该方法可以反映电液伺服阀的动态特性,同时可以方便地通过参数修正对电液伺服阀进行优化设计,为电液伺服工程技术人员提供了电液伺服阀设计的更为高效便捷的手段。

基于Modelica的MEMS系统级多领域建模与仿真

分析了现有的MEMS系统级建模与仿真方法,讨论了运用Modelica语言进行面向对象的非因果关系建模方法,建立了基于Modelica的电容式微型静电致动器系统级模型,仿真结果证明了Modelica用于MEMS系统级多领域仿真的可行性。

融合关系标签和位置信息的中文医疗文本因果关系抽取方法研究

目的/意义利用因果关系词相对位置辅助深度学习模型,提高因果关系预测能力,挖掘医疗文本增益信息。方法/过程将医疗文本因果关系词相对位置信息表示为关系特征层嵌入预训练语言模型,融合基线模型进行实体识别及关系抽取。结果/结论嵌入关系特征层的模型F1值较基线模型BERT-BiLSTM-CRF和CasRel分别提升2.92个百分点和6.41个百分点,因果关系预测能力较好。

基于Modelica/Dymola的微谐振器建模与仿真

针对MEMS的多领域耦合和系统级的快速建模与仿真要求,提出研究关于微型梳状静电谐振器建模与仿真方法。为提高系统的稳定性,减少误差,采用了基于Modelica/Dymola的非因果关系建模方法及流程,以梳状谐振器机电耦合模型的自然形式方程为基础,以Modelica语言建立非因果关系的仿真模型,借助于Dymola平台对振子的位移/速度曲线进行仿真。仿真结果与理论验证相符,表明Modelica具有建模过程简单、建模速度快和仿真精度高等优点,适合于MEMS多领域建模与仿真研究。

基于线性结构因果模型的服务故障传播路径识别

在云计算环境中,当一个服务发生故障时,故障会随着服务之间的交互行为不断传播,导致产生大规模服务失效的风险。复杂且动态变化的服务调用关系加大了识别服务故障传播路径的难度。针对该问题,提出一种基于因果图的服务故障传播路径识别方法。监测并收集服务的运行数据,通过服务运行数据对服务故障事件完成度量;根据因果图模型推断服务故障事件之间的因果关系并构建服务故障传播图;利用服务故障传播图确定故障传播路径。实验结果表明,该方法能够有效识别服务故障传播路径。