Comparative assessment of statistical and machine learning techniques towards estimating the risk of developing type 2 diabetes and cardiovascular complications

The aim of the present study is to comparatively assess the performance of different machine learning and statistical techniques with regard to their ability to estimate the risk of developing type 2 diabetes mellitus (Case 1) and cardiovascular disease complications (Case 2). This is the first work investigating the application of ensembles of artificial neural networks (EANN) towards producing the 5‐year risk of developing type 2 diabetes mellitus and cardiovascular disease as a long‐term diabetes complication. The performance of the proposed models has been comparatively assessed with the performance obtained by applying logistic regression, Bayesian‐based approaches, and decision trees. The models' discrimination and calibration have been evaluated using the classification accuracy (ACC), the area under the curve (AUC) criterion, and the Hosmer–Lemeshow goodness of fit test. The obtained results demonstrate the superiority of the proposed models (EANN) over the other models. In Case 1, EANN with different topologies has achieved high discrimination and good calibration performance (ACC = 80.20%, AUC = 0.849, p value = .886). In Case 2, EANN based on bagging has resulted in good discrimination and calibration performance (ACC = 92.86%, AUC = 0.739, p value = .755).     

Biomarkers for cardiovascular risk assessment in autoimmune diseases

Autoimmune diseases, such as antiphospholipid syndrome, systemic lupus erythematosus, and rheumatoid arthritis, are characterized by a high prevalence of cardiovascular (CV) disease (CVD), which constitutes the leading causes of morbidity and mortality among such patients. Although such effects are partly explained by a higher prevalence of traditional CV risk factors, many studies indicate that such factors do not fully explain the enhanced CV risk in these patients. In addition, risk stratification algorithms based upon traditional CV risk factors are not as predictive in autoimmune diseases as in the general population. For these reasons, the timely and accurate assessment of CV risk in these high-risk populations still remains an unmet clinical need. An enhanced contribution of different inflammatory components of the immune response, as well as autoimmune elements (e.g. autoantibodies, autoantigens, and cellular response), has been proposed to underlie the incremental CV risk observed in these populations. Recent advances in proteomic tools have contributed to the discovery of proteins involved in CVDs, including some that may be suitable to be used as biological markers. In this review we summarize the main markers in the field of CVDs associated with autoimmunity, as well as the recent advances in proteomic technology and their application for biomarker discovery in autoimmune disease.     

Probabilistic approximations of ODEs based bio-pathway dynamics

Bio-chemical networks are often modeled as systems of ordinary differential equations (ODEs). Such systems will not admit closed form solutions and hence numerical simulations will have to be used to perform analyses. However, the number of simulations required to carry out tasks such as parameter estimation can become very large. To get around this, we propose a discrete probabilistic approximation of the ODEs dynamics. We do so by discretizing the value and the time domain and assuming a distribution of initial states w.r.t. the discretization. Then we sample a representative set of initial states according to the assumed initial distribution and generate a corresponding set of trajectories through numerical simulations. Finally, using the structure of the signaling pathway we encode these trajectories compactly as a dynamic Bayesian network.This approximation of the signaling pathway dynamics has several advantages. First, the discretized nature of the approximation helps to bridge the gap between the accuracy of the results obtained by ODE simulation and the limited precision of experimental data used for model construction and verification. Second and more importantly, many interesting pathway properties can be analyzed efficiently through standard Bayesian inference techniques instead of resorting to a large number of ODE simulations. We have tested our method on ODE models of the EGF-NGF signaling pathway [1] and the segmentation clock pathway [2]. The results are very promising in terms of accuracy and efficiency.     

基于贝叶斯方法的知识发现

贝叶斯方法是概率统计学中一种很重要的。方法贝叶网络就是根据各个变量之间概率关系用图论方法建立的模型,本文概率统计图的贝叶斯规则应用于知识发现。建立图论模型进行数据挖掘,文章最后应用贝叶斯网络对于实际的数据库进行知识发现,其结果说明了这种方法的有效性。     

一种基于混合遗传的贝叶斯网络分解算法

针对最优贝叶斯网络分解是一个NP-完全问题,提出了一种基于混合遗传贝叶斯网络分解算法PHGA.PHGA算法将进化过程划分为三个不同的阶段,在前期和中期阶段采用较大的种群规模和交叉率,以及较小的群体选择压力,来增强PHGA算法的全局探索能力,避免早熟现象;在后期采用较小的种群规模和交叉率,以及较大的群体选择压力,并引入爬山局部优化算子,以增强群体在进化后期中的局部寻优能力,提高算法的收敛速度.三个标准的贝叶斯网络上的实验表明该算法在最优解方面要优于遗传算法和模拟退火算法.     

Extracting new patterns for cardiovascular disease prognosis

Abstract: Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis.     

Bibliometric analysis on cardiovascular disease treated by traditional Chinese medicines based on big data

In recent years, with the ageing of the population, environmental degradation and dietary abnormalities, the incidence of global cardiovascular disease has increased sharply, its serious impact on physical and mental health of humans is an important factor. Chinese medicine has a long history and significant efficacy in the treatment of cardiovascular disease. Based on the bibliometric method in the context of big data, this paper searched and screened the relevant literatures of TCM in the treatment of cardiovascular diseases included by SCI from 2004 to 2016 and analysed them from the aspects of annual change, regional distribution, institutional distribution, journal source, research field, highly cited literatures, high-frequency keywords and so on. The analysis results show that the total amount of literature in the treatment of cardiovascular diseases by traditional Chinese medicine is on the rise, mainly focusing on the application of traditional Chinese medicine and Chinese herbal compound, acupuncture and moxibustion, and taiji and other traditional Chinese medicine regimen. The research results of this paper provide a direction reference for TCM researchers to better understand the current research status of TCM in the treatment of cardiovascular diseases under the background of big data and for further research.     

Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling

A Bayesian selective combination method is proposed for combining multiple neural networks in nonlinear dynamic process modelling. Instead of using fixed combination weights, the probability of a particular network being the true model is used as the combination weight for combining that network. The prior probability is calculated using the sum of squared errors of individual networks on a sliding window covering the most recent sampling times. A nearest neighbour method is used for estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. Forward selection and backward elimination are used to select the individual networks to be combined. In forward selection, individual networks are gradually added into the aggregated network until the aggregated network error on the original training and testing data sets cannot be further reduced. In backward elimination, all the individual networks are initially aggregated and some of the individual networks are then gradually eliminated until the aggregated network error on the original training and testing data sets cannot be further reduced. Application results demonstrate that the proposed techniques can significantly improve model generalisation and perform better than aggregating all the individual networks.     

高效计算因果网中的最大可能解释

在因果网中,高效计算的最大可能解释(Most Probable Explanations,MPE)是一个关键问题。从有向无环图的角度,研究者们发现每一个因果网都有一个与之对应的贝叶斯网络。文中通过比较干预和微分的语义,揭示了MPE完全原子干预的微分语义。根据微分语义,因果网中原子干预MPE实例的计算可以归约为贝叶斯网络中的MPE实例的计算。接着,提出了一个联合树算法(Best JoinTree,BJT),它通过在因果网中只构建一个联合树来计算最好的原子干预,原子干预的结果包含一个BMPE(Best MPE)概率和它对应的实例。其中,BMPE概率是对MPE所有结点分别进行原子干预后得到的最高概率。BJT可以采用干预的效果来计算对应贝叶斯网络的MPE概率和MPE实例。最后,实验证实了绝大多数因果网在计算最好原子干预时,BJT的速度比目前最好的算法快了超过10倍。     

A modular design of Bayesian networks using expert knowledge: Context-aware home service robot

Recently, demand for service robots increases, and, particularly, one for personal service robots, which requires robot intelligence, will be expected to increase more. Accordingly, studies on intelligent robots are spreading all over the world. In this situation, we attempt to realize context-awareness for home robot while previous robot research focused on image processing, control and low-level context recognition. This paper uses probabilistic modeling for service robots to provide users with high-level context-aware services required in home environment, and proposes a systematic modeling approach for modeling a number of Bayesian networks. The proposed approach supplements uncertain sensor input using Bayesian network modeling and enhances the efficiency in modeling and reasoning processes using modular design based on domain knowledge. We verify the proposed method is useful as measuring the performance of context-aware module and conducting subjective test.     

Modeling the influence of risk factors and chronic diseases on the development of strokes and peripheral arterial-vascular disease

Several chronic diseases and risky lifestyles have become an important social burden in many countries around the world, a problem which should not be underestimated. The situation is even more worrying when we take into account their influence on serious health complications that can threaten patients’ lives or significantly reduce their quality of life. The presented modeling-and-simulation study enables us to estimate the number of patients with type-2 diabetes, hypercholesterolemia and hypertension, obese and smoking people and the influence of these conditions on the development of strokes and peripheral arterial-vascular diseases. In addition, an estimate of treatment costs makes it possible to evaluate the social burden and provide information about the potential savings resulting from treating intensive chronic diseases.     

面向可信软件的风险管理模型研究

:进行了面向可信软件的风险管理模型的研究。首先采用贝叶斯信仰网络对影响软件可信性的风险进行了分析 ;其次 ,建立了基于约束满足的风险控制模型。该软件项目风险管理研究为提高软件质量 ,实现软件的可信性提供了新思路。     

面向可信软件的风险评估方法

风险管理逐渐成为开发高质量软件过程中的重要的组成部分。风险评估作为风险管理的重要活动之一,是风险控制的前提。贝叶斯网络作为风险管理的有力工具之一,是处理不确定性的有效方法。结合贝叶斯网络与模糊理论,提出一种风险评估方法,首先使用贝叶斯网络对影响可信软件的风险因素进行风险概率评估,然后利用模糊综合评价法进行风险综合影响评估。该方法用于软件项目的风险评估,为开发高质量的可信软件提供新策略。     

An efficient heuristic for selecting active nodes in wireless sensor networks

Energy saving is a paramount concern in wireless sensor networks (WSNs). A strategy for energy saving is to cleverly manage the duty cycle of sensors, by dynamically activating different sets of sensors while non-active nodes are kept in a power save mode. We propose a simple and efficient approach for selecting active nodes in WSNs. Our primary goal is to maximize residual energy and application relevance of selected nodes to extend the network lifetime while meeting application-specific QoS requirements. We formalize the problem of node selection as a knapsack problem and adopt a greedy heuristic for solving it. An environmental monitoring application is chosen to derive some specific requirements. Analyses and simulations were performed and the impact of various parameters on the process of node selection was investigated. Results show that our approach outperforms a naı¨ve scheme for node selection, achieving large energy savings while preserving QoS requirements.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

Unravelling infectious disease eco-epidemiology using Bayesian networks and scenario analysis: A case study of leptospirosis in Fiji

Regression models are the standard approaches used in infectious disease epidemiology, but have limited ability to represent causality or complexity. We explore Bayesian networks (BNs) as an alternative approach for modelling infectious disease transmission, using leptospirosis as an example. Data were obtained from a leptospirosis study in Fiji in 2013. We compared the performance of naïve versus expert-structured BNs for modelling the relative importance of animal species in disease transmission in different ethnic groups and residential settings. For BNs of animal exposures at the individual/household level, R² for predicted versus observed infection rates were 0.59 for naïve and 0.75–0.93 for structured models of ethnic groups; and 0.54 for naïve and 0.93–1.00 for structured models of residential settings. BNs provide a promising approach for modelling infectious disease transmission under complex scenarios. The relative importance of animal species varied between subgroups, with important implications for more targeted public health control strategies.     

信息安全风险管理理论在IP网络中的应用

该文从信息安全管理的理念、方法学和相关技术入手,通过分析IP网络结构特征及其安全管理的内容,提出基于IP网络的安全管理及风险评估的主要实践步骤及其要点分析。     

Participatory modelling for stakeholder involvement in the development of flood risk management intervention options

Advancing stakeholder participation beyond consultation offers a range of benefits for local flood risk management, particularly as responsibilities are increasingly devolved to local levels. This paper details the design and implementation of a participatory approach to identify intervention options for managing local flood risk. Within this approach, Bayesian networks were used to generate a conceptual model of the local flood risk system, with a particular focus on how different interventions might achieve each of nine participant objectives. The model was co-constructed by flood risk experts and local stakeholders. The study employs a novel evaluative framework, examining both the process and its outcomes (short-term substantive and longer-term social benefits). It concludes that participatory modelling techniques can facilitate the identification of intervention options by a wide range of stakeholders, and prioritise a subset for further investigation. They can help support a broader move towards active stakeholder participation in local flood risk management.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

CPS中的一种KID组合优化算法

由于信息物理融合系统中网络之间的依赖关系,使得连锁故障现象频繁出现,系统变得脆弱,甚至可能引起网络瘫痪.如何减少连锁故障现象的发生是研究的重点内容.提出一种KID组合优化算法,通过寻求网络中节点的双向外连接数目K、网络内聚度ID(intra-degree)与阈值Pc值(维持网络运行所需要的最小值)的关系,找到K与ID如何组合使得Pc值降低,从而达到减少连锁故障现象的发生、提高网络稳定性的目的.从仿真可以看出:在ID一定的情况下,可以改变K值,使得阈值Pc最低,此时的系统稳定性最好;在相同K、ID组合的情况下该算法比随机分配、单向外连接边等算法更能提高系统的稳定性.