关键词： 数据挖掘   WEKA平台   慢性肾病   Random   Fores  

摘要： 目的:随着医院信息化建设的发展,如何从海量医院临床信息挖掘出隐藏的关联数据,为患者提供早期辅助诊断,是目前智能医疗诊断领域热门的研究方向。方法:在WEKA数据挖掘平台的基础上,对慢性肾脏疾病数据进行挖掘分析,并比较不同挖掘算法的分类准确性。结果:对比实验结果表明,较于其他分类器,Random Forest分类器对慢性肾脏疾病数据集具有较好的分类准确性。结论:基于WEKA数据挖掘平台的医学数据最适合肾脏疾病诊断和预测的算法,为后期医疗行业的大数据分析及挖掘提供新思路。

关键词： 无线传感网络   最小二乘法   时钟同步   时钟偏差   频率偏差   均方误差  

摘要： 提出一种无线传感网络同步的方法,结合发送者-接收者同步(SRS)和接收者-接收者同步(RRS)两种方法,使得位于特定区域的传感器节点通过监听一对超级传感器节点交换时钟消息,借助最小二乘法精确估计出时钟偏差以及频率偏差进行补偿,实现节点间同步。该时钟同步方案的设计与其他已提出的同步方案相比,显著降低了整个网络的能量消耗而不引起任何同步精度的损失。

摘要： Introduction Patients with major depressive disorder (MDD) are likely to experience stress and have a higher risk of developing aging-related diseases [1]. Globally, an estimated 350 million people of all ages suffer from a depression. At the same time, we are facing an aging society, in which manifestations of depression and aging are linked to decreased health, decreased quality of life, and increased healthcare costs. This underlines the importance of developing targeted therapeutic interventions in pursuance of a more healthy and happy society. More evidence is emerging showing that chronological age and biological age may be two different processes that can even diverge. MDD patients seem to feel older and age faster than healthy people of the same age. Current machine learning methods can accurately predict chronological age with correlations of 0.8-0.95 from epigenetics, transcriptomics, proteomics, metabolomics, brain, and other biological data [2]. By calculating the difference between one’s estimated biology-based age and chronological age, one can translate a complex multidimensional aging pattern across biology into one outcome [3]: “the age gap”. A positive age gap represents accelerated aging, whereas a negative brain age gap represents decelerated aging. Previous studies have shown biological age gap associations with physical and cognitive fitness, psychiatric and somatic diseases, health and lifestyle variables, and mortality [4], providing biologically meaningful *** Within the Netherlands Study of Depression and Anxiety (NESDA) [5] we also developed such a biology-based age, specifically one based on DNA methylation patterns (DNAmAge). We selected DNA methylation levels of all 28 million CpG sites in blood as features to predict chronological age in N=1130 subjects (n=811 MDD), using ridge regression. The predictive power was estimated using 10-fold cross-validation. The correlation between chronological age and DNA methylation age est

关键词： Zebrafish   Stress   Meta-analysis   PCA   PLS-DA   R-SVM  

摘要： In order to understand. the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present study is aimed at identifying the effect of abiotic and biotic stress factors, and it is found that several stress responsive genes are common for both abiotic and biotic stress factors in zebrafish. The meta-analysis of micro-array studies revealed that almost 4.7% i.e., 108 common DEGs are differentially regulated between abiotic and biotic stresses. This shows that there is a global coordination and fine-tuning of gene regulation in response to these two types of challenges. We also performed dimension reduction methods, principal component analysis, and partial least squares discriminant analysis which are able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of DEGs. Beside these methods, the random forests decision tree model classified five out of 8 stress conditions with high accuracy. Finally, Functional enrichment analysis revealed the different gene ontology terms, transcription factors and miRNAs factors in the regulation of stress responses.

摘要： The use of retrospective health care claims datasets is frequently criticized for the lack of complete information on potential confounders. Utilizing patient's health status-related information from claims datasets as surrogates or proxies for mismeasured and unobserved confounders, the high-dimensional propensity score algorithm enables us to reduce bias. Using a previously published cohort study of postmyocardial infarction statin use (1998-2012), we compare the performance of the algorithm with a number of popular machine learning approaches for confounder selection in high-dimensional covariate spaces: random forest, least absolute shrinkage and selection operator, and elastic net. Our results suggest that, when the data analysis is done with epidemiologic principles in mind, machine learning methods perform as well as the high-dimensional propensity score algorithm. Using a plasmode framework that mimicked the empirical data, we also showed that a hybrid of machine learning and high-dimensional propensity score algorithms generally perform slightly better than both in terms of mean squared error, when a bias-based analysis is used.

关键词： Landslide susceptibility modeling   Machine learning   Support vector machine (SVM)   Artificial neural network (ANN)   Logistic regression (LR)  

摘要： Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

关键词： 巨大儿   超声测量   机器学习  

摘要： 目的:应用机器学习方法,提高预测巨大儿的准确性。方法:查阅2015年1月至2016年12月我院产科巨大儿及正常体质量新生儿病历,获取新生儿出生体质量及超声测量数据。以新生儿实际出生体质量为金标准,评价并比较超声内置的Hadlock公式、多元线性回归以及k近邻、支持向量机、随机森林分类的机器学习方法对巨大儿预测的准确性。结果:内置的Hadlock公式预测巨大儿的灵敏度为40.86%,Youden指数为39.95%;多元线性回归预测巨大儿的灵敏度为60.22%,Youden指数为58.85%;机器学习法中k近邻预测巨大儿的灵敏度86.21%,Youden指数为75.10%;支持向量机预测巨大儿的灵敏度86.21%,Youden指数为73.51%;随机森林预测巨大儿的灵敏度81.03%,Youden指数为71.51%。多元线性回归模型方法的Youden指数大于超声内置公式方法,差异有统计学意义(u=3.64,P<0.001);机器学习法中k近邻、支持向量机、随机森林分类的Youden指数大于超声内置公式的Youden指数(P<0.001),k近邻、支持向量机的Youden指数大于多元线性回归模型法(P<0.05)。结论:机器学习方法预测巨大儿的准确性较高,具有参考应用价值。

关键词： Mobile Robotics   Localization   Omnidirectional Sonar   Machine Learning  

摘要： In the past few years, the mobile robotics has presented a growth in the research field, due to its high application area. The robot localization and its displacement in an unknown environment are fundamental to its operation. Furthemore, signal processing techniques and machine learning methods have been widely appled along with sensors into mobile robot mapping, localization and navigation. In this paper, a low cost and efficient approach for mobile robot localization using sensing by omnidirectional sonar along with signal processing techniques and machine learning methods is proposed. Regarding to classifiers, Bayes classifier, Multilayer Perceptron (MLP), k-Nearest Neighbors kNN, Support Vector Machines (SVM) and Optimum Path Forest (OPF) were analyzed. According to results, OPF using Canberra distance was the best method with accuracy of 98.13 percent and test time of 17.4us.

关键词： Variable annuity   Johnson curve   Machine learning   Dollar delta   VaR  

摘要： Variable annuity (VA) with embedded guarantees have rapidly grown in popularity around the world in recent years. Valuation of VAs has been studied extensively in past decades. However, most of the studies focus on a single contract. These methods can be extended neither to valuate nor to manage the risk of a large variable annuity portfolio due to the computational complexity. In this paper, we propose an efficient moment matching machine learning method to solve this problem. This method is proved to be a good candidate for risk management in terms of the speed of and the complexity of computing the annual dollar deltas, VaRs and CVaRs for a large variable annuity portfolio whose contracts are over a period of 25 years. There are two stages for our method. First, we select a small number of contracts and propose a moment matching Monte Carlo method based on the Johnson curve, rather than the well known nested simulations, to compute the annual dollar deltas, VaRs and CVaRs for each selected contract. Then, these computed results are used as a training set for well known machine learning methods, such as regression tree, neural network and so on. Afterwards, the annual dollar deltas, VaRs and CVaRs for the entire portfolio are predicted through the trained machine learning method. Compared to other existing methods Bauer et al. (2008);Gan (2013);Gan and Lin (2015), our method is very efficient and accurate. Finally, our test results support our claims. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Reliability prediction   Machine learning   Censored data   Weibull distribution  

摘要： The reliability estimation of engineered components is fundamental for many optimization policies in a production process. The main goal of this paper is to study how machine learning models can fit this reliability estimation function in comparison with traditional approaches (e.g., Weibull distribution). We use a supervised machine learning approach to predict this reliability in 19 industrial components obtained from real industries. Particularly, four diverse machine learning approaches are implemented: artificial neural networks, support vector machines, random forest, and soft computing methods. We evaluate if there is one approach that outperforms the others when predicting the reliability of all the components, analyze if machine learning models improve their performance in the presence of censored data, and finally, understand the performance impact when the number of available inputs changes. Our experimental results show the high ability of machine learning to predict the component reliability and particularly, random forest, which generally obtains high accuracy and the best results for all the cases. Experimentation confirms that all the models improve their performance when considering censored data. Finally, we show how machine learning models obtain better prediction results with respect to traditional methods when increasing the size of the time-to-failure datasets.