A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

THE IMPORTANCE OF NEUTRAL EXAMPLES FOR LEARNING SENTIMENT

Most research on learning to identify sentiment ignores "neutral" examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.     

高精度合成地震记录制作技术研究

制作高精度的合成地震记录是精细储层描述的基础，本文在分析合成地震记录原理的基础上，从地震剖面的极性判断、平均速度的选取，反射系数的准确提取，子波的精确估算等方面对制作合成记录的要点进行了分析，通过提高以上四方面的精度，提高了制作合成地震记录的精度，并提出了一套可行的合成地震记录的检查方法。     

绥高#2线线路保护2动作分析

对高岭站2009年6月15日绥高#2线线路保护动作的原因进行深入分析,介绍处理措施。     

融合关键对象识别与深层自注意力的Bi-LSTM情感分析模型

在线评论文本通常涉及多个评价对象,对象的表达方式有显式和隐式之分,针对不同对象的情感倾向可能不会完全一致.关键评价对象是评论中最受关注的对象,其相应的情感语义对整条评论的情感观点起主导作用.本文构建了融合关键对象识别与深层自注意力机制的Bi-LSTM模型,以提升短文本情感分类的效果.使用CNN处理文本,基于卷积层输出结果识别关键评价对象,并在此基础上完成深层自注意力的学习.将对象信息与文本信息进行融合,利用注意力机制强化的Bi-LSTM模型得到评论文本的情感分类结果.在酒店评论数据集上进行实验,与之前基于深度学习的模型相比,本文方法在精确率、召回率和F-score评价指标方面均有更好的表现.     

一种面向目标的情感极性分析方法

面向目标的情感分析是细粒度情感分析的重要任务之一,旨在预测句子中给定目标实体的情感极性.当前大多数研究方法忽略了句法结构信息,在情感判别时往往会关注无关词汇,从而使分类性能下降.为此,设计了一种新的引入句法结构的模型,该模型利用双向预训练编码器和作用于依存句法树的图卷积网络分别捕获文本的上下文信息和句法结构信息,并使用多头注意力机制进行信息聚合得到目标的情感分类表征.此外,还将该模型与现有的领域自适应方法相结合,同时向模型中引入领域知识和句法结构知识,进一步提升了模型效果.在几个常用的标准数据集上的实验结果表明了上述模型的有效性.     

某1000 MW汽轮机组励磁系统实测建模过程浅析

该机组是上海汽轮发电机有限公司生产的1000 MW汽轮机组,励磁调节器是ABB公司生产的UNITROL-6800型数字励磁调节器。文章论述了该机组励磁系统参数测试结果,给出了电力系统稳定计算用励磁系统的模型和参数。     

直流预压对针板电极下XLPE中空间电荷及电树枝的影响

空间电荷是影响高压直流电缆绝缘电树枝特性的主要原因之一。基于双极性载流子输运模型,对±20 kV、±22.5 kV和±25 kV直流预压下3 600 s内二维针板电极模型空间电荷分布进行仿真分析,并对比分析空间电荷分布与直流接地电树枝引发特性。结果表明：空间电荷浓度及注入深度随预压幅值及时间的增加而增大;直流接地电树枝引发长度随预压时间及幅值的增加而增加。空间电荷注入深度与电树枝引发长度两者之间高度相似。接地电树枝引发特性存在差异的主要原因是针尖附近空间电荷的分布特性。