基于多标签CRF的疾病名称抽取*

生物医疗文本中的命名实体识别对于构建和挖掘大型临床数据库以服务于临床决策具有重要意义,而其中一个基础工作是疾病名称的识别。医疗文本中存在大量的复合疾病名称,难以分离抽取出其中的实体。针对这一问题,提出一种基于多标签的条件随机场算法,首先对数据标注多层标签,每层标签针对复合疾病名称中的不同疾病,然后用整合后的最终标签去训练模型,最后再对模型预测的标签进行分离。此方法能够识别传统条件随机场算法无法识别的复合疾病名称,实验结果验证了所提算法的有效性。     

基于条件随机场的网络评论与事件中命名实体匹配研究

为发现针对新闻事件中实体展开的网络评论,本文提出一种基于条件随机场的网络评论与新闻事件中命名实体匹配方法。提出使用Semi-Markov CRFs从评论语句中识别出片段粒度的命名实体。针对评论描述随意的特点,结合命名实体的模式特征、符号特征等特征识别出评论中实体的简称、缩写、昵称等变体形式。本文使用Linear-Chain CRFs结合多种匹配方法计算评论中命名实体与事件中命名实体的综合相似度,完成匹配。实验证明,提出的基于条件随机场的网络评论与事件中命名实体匹配方法能够准确根据命名实体匹配评论与事件。     

基于CCRF的领域本体概念上下位关系抽取

现有领域本体概念上下位关系抽取方法受到手工标注和特定模式的限制。针对该问题,提出一种基于层叠条件随机场的领域本体概念上下位关系抽取方法。以自由文本为抽取对象,采用两层条件随机场算法,将训练数据处理成条件随机场能识别的线性结构。低层条件随机场模型考虑词之间的长距离依赖,对词进行建模,识别出领域概念并对概念进行顺序组合,结合模板定义特征得到概念对;高层模型对成对概念进行上下位语义标注,识别出领域本体概念之间的上下位关系。采用真实语料进行实验,结果表明,该方法具有较好的识别效果。     

基于深度学习的网购评论命名实体识别方法

针对网购评论命名实体识别中重要词汇被忽略的问题,在评论短文本处理基础上,借鉴多头注意力机制、词汇贡献度和双向长短时记忆条件随机场提出一种基于MA-BiLSTM-CRF模型的网购评论命名实体识别方法。首先,用词向量和词性向量的组合来表示评论文本语义信息;其次,用BiLSTM提取文本特征;然后,引入多头注意力机制从多层面、多角度提升模型性能;最后,用条件随机场（CRF）识别命名实体。实验结果表明,该方法能提升网购评论实体识别效果。     

基于层叠条件随机场的CFN自动标注研究

提出了一种基于层叠条件随机场的CFN自动标注方法。该方法在低层条件随机场模型中解决了框架元素的识别,将识别结果传递到上层短语类型识别的条件随机场模型,再将识别结果传递到上层句法功能识别的条件随机场模型,其低层模型为上层模型提供决策支持。实验选用CFN中"陈述"框架下的句子库,实现了基于层叠条件随机场CFN自动标注的原型系统。     

基于条件随机场的矿工检测方法

针对现有视频图像目标检测算法应用于矿工检测时检出率、定位准确率、检测效率等均较低的问题,提出了一种基于条件随机场的矿工检测方法。该方法包括矿工检测模型建立与矿工检测识别2部分。在模型建立阶段,提取若干样本图像的方向梯度直方图特征,并利用主成分分析法对特征进行降维处理;以条件随机场为框架进行感兴趣区域标志,以标定训练样本,并训练条件随机场模型参数。在检测识别阶段,提取待检测图像的方向梯度直方图特征,并对特征进行降维,采用训练得到的条件随机场模型,通过局部二元模式推断标定图像各子窗口,最终得到矿工所在区域。实验结果表明,该方法可准确地检测出矿工在图像中的位置。     

条件随机场模型在中文人名识别中的研究与实现

中文人名的识别至今还是自然语言研究领域一个比较困难的课题．因此提出一种基于条件随机场模型的文中人名识别方法。条件随机场模型是一种无向图模型．有效避免有向图在标记的过程中出现偏执的问题,并且通过二次识别．有效解决人名在上下文环境中的识别问题。通过实验分析,基于条件随机场模型的人名识别能比较准确地识别出中文的人名。     

基于高阶CRF模型的图像语义分割

图像语义分割方法大多基于点对条件随机场模型, 不能定位到单个目标, 并且难以利用全局形状特征, 造成误识。针对这些问题, 提出一种新的高阶条件随机场模型, 将基于全局形状特征的目标检测结果和点对条件随机场模型统一在一个概率模型框架中, 同时完成图像分割、目标检测与识别的任务。利用目标检测器和前背景分割算法获取图像中目标区域, 在目标区域上定义新的高阶能量项。新的高阶条件随机场模型就是高阶能量项和点对条件随机场模型的加权混合模型, 其最优解即为图像语义分割结果。在MSRC-21类数据库上进行的实验验证了该模型能够显著提升图像语义分割性能, 并定位到单个目标。     

基于交互式条件随机场的RGB-D图像语义分割

RGB-D图像语义分割是场景识别与分析的基础步骤,基于条件随机场(CRF)的图像分割方法不能有效应用于复杂多变的现实场景,因此提出一种交互式条件随机场的RGB-D图像语义分割方法。首先利用中值滤波和形态重构方法对Kinect相机拍摄的RGB-D图像进行预处理,降低图像噪声及数据缺失;其次,利用基于条件随机场的分割方法对经过预处理的图像进行自动分割,得到粗略的分割结果;最后,用户通过交互平台,将代表正确场景信息的标签反应到条件随机场模型中并进行模型更新,改善分割结果。通过多组实验验证了该算法不仅满足用户对于复杂场景分割与识别的需求,而且用户交互简单、方便、直观。相较于传统的基于条件随机场分割方法,该方法得到较高的分割精度和较好的识别效果。     

基于条件随机场的评价对象缺省项识别

在电商网站评论文本中,评价对象和评价属性的缺省识别对文本情感分析具有重要地作用。针对电商网站评论文本中评价对象和评价属性缺省问题,该文提出了一种基于条件随机场的评价对象缺省项识别方法。首先利用情感词典识别观点句,将缺省项识别问题转换成序列标注问题,综合词法特征和依存句法特征,使用条件随机场模型进行训练,并在测试集上对待识别的观点句进行序列标注,通过标注结果判定缺省项的位置。实验结果表明,该方法具有较高的准确率和召回率,验证了该方法的有效性。     

Temporality and Context for detecting adverse drug reactions from longitudinal data

This paper introduces a method for mining co-occurring events from longitudinal data, and applies this method to detecting adverse drug reactions (ADRs) from patient data. Electronic health records are richer than older data sources (such as spontaneous report records) and thus are ideal for ADR mining. However, current data mining methods, such as disproportionality ratios and temporal itemset mining, ignore certain important aspects of the longitudinal data in patient records. In this paper, we highlight two specific problems with current methods, which we name temporal and contextual sensitivity, and discuss why these two properties are vital to mining patterns from longitudinal data. We also propose two sensitive longitudinal rate comparison measures, which utilize condition occurrence rates and length of drug eras, for mining ADRs from this type of data. These novel methods are then used to rank potential ADRs, along with existing state-of-the-art methods, under many simulated yet realistic datasets. In 48 out of 60 experiments, the proposed longitudinal rate comparison methods significantly outperform other methods in mining known ADRs from other drug / condition pairs.     

面向社交网络的潜在药物不良反应发现

随着互联网的发展,社交网络中积累了大量的医疗健康领域的文本数据。该文利用基于信息熵的方法,从健康社交网络中的用药者评论数据中识别药物的潜在不良反应;同时,对于潜在药物不良反应,该文提出了基于Word2vec和Skip-gram模型的蛋白质关联紧密度函数,尽最大努力发现药物引起其“潜在”不良反应的证据链。实验证明,该方法用来寻求潜在药物不良反应证据链是有效的。     

基于简单名词短语的汉语介词短语识别研究

该文提出一种融入简单名词短语信息的介词短语识别方法。该方法首先使用CRF模型识别语料中的简单名词短语,并使用转换规则对识别结果进行校正,使其更符合介词短语的内部短语形式;然后依据简单名词短语识别结果对语料进行分词融合;最后,通过多层CRFs模型对测试语料进行介词短语识别,并使用规则进行校正。介词短语识别的精确率、召回率及F-值分别为: 93.02%、92.95%、92.99%,比目前发表的最好结果高1.03个百分点。该实验结果表明基于简单名词短语的介词短语识别算法的有效性。
     

Comparison of algorithms that detect drug side effects using electronic healthcare databases

The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Health Improvement Network (THIN) database is an electronic healthcare database containing medical information on over 11 million patients that has excellent potential for detecting ADRs. In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families. This is the first comparison of ADR signalling algorithms that includes MUTARA and HUNT and enabled us to set a benchmark for the adverse drug reaction signalling ability of the THIN database. The drugs were selectively chosen to enable a comparison with previous work and for variety. It was found that no algorithm was generally superior and the algorithms’ natural thresholds act at variable stringencies. Furthermore, none of the algorithms perform well at detecting rare ADRs.     

A distributed adverse drug reaction detection system using intelligent agents with a fuzzy recognition‐primed decision model

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is highly desirable. Nevertheless, current postmarketing surveillance methods largely rely on spontaneous reports that suffer from serious underreporting, latency, and inconsistent reporting. Thus these methods are not ideal for rapidly identifying rare ADRs. The multiagent systems paradigm is an emerging and effective approach to tackling distributed problems, especially when data sources and knowledge are geographically located in different places and coordination and collaboration are necessary for decision making. In this article, we propose an active, multiagent framework for early detection of ADRs by utilizing electronic patient data distributed across many different sources and locations. In this framework, intelligent agents assist a team of experts based on the well‐known human decision‐making model called Recognition‐Primed Decision (RPD). We generalize the RPD model to a fuzzy RPD model and utilize fuzzy logic technology to not only represent, interpret, and compute imprecise and subjective cues that are commonly encountered in the ADR problem but also to retrieve prior experiences by evaluating the extent of matching between the current situation and a past experience. We describe our preliminary multiagent system design and illustrate its potential benefits for assisting expert teams in early detection of previously unknown ADRs. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 827–845, 2007.     

基于遗传神经网络的抗肿瘤药物不良反应诊断系统

提出一种以遗传神经网络为基础的抗肿瘤药物不良反应(ADR)诊断系统,并以此考察了LEVF方案引起的骨髓抑制毒性,筛选了相关的各种指标,做出了较好的预测。解决了神经网络结构选择和“过度拟合”问题。该系统既可以用于临床的ADR诊断,也可以用于新药的ADR事件评价。     

Drug identification and interaction checker based on IoT to minimize adverse drug reactions and improve drug compliance

Drug compliance and adverse drug reactions (ADR) are two of the most important issues regarding patient safety throughout the worldwide healthcare sector. ADR prevalence is 6.7 % throughout hospitals worldwide, with an international death rate of 0.32 % of the total of the patients. This rate is even higher in Ambient Assisted Living environments, where 15 % of the patients suffer clinically significant interactions due to patient non-compliance to drug dosage and schedule of intake in addition to suffering from polypharmacy. These instances increase with age and cause risks of drug interactions, adverse effects, and toxicity. However, with a tight follow-up of the drug treatment, complications of incorrect drug use can be reduced. For that purpose, we propose an innovative system based on the Internet of Things (IoT) for the drug identification and the monitoring of medication. IoT is applied to examine drugs in order to fulfill treatment, to detect harmful side effects of pharmaceutical excipients, allergies, liver/renal contradictions, and harmful side effects during pregnancy. The IoT design acknowledges that the aforementioned problems are worldwide so the solution supports several IoT identification technologies: barcode, Radio Frequency Identification, Near Field Communication, and a new solution developed for low-income countries based on IrDA in collaboration with the World Health Organization. These technologies are integrated in personal devices such as smart-phones, PDAs, PCs, and in our IoT-based personal healthcare device called Movital.     

Taskmaster: an interactive, graphical environment for task specification, execution and monitoring

Taskmaster is an interactive environment that employs a unique blend of graphic technologies and iconic images to support user task specification. In this environment, problem solving is based on the selection, specification, and composition of tools that correspond to natural sets of ordered operations. The Taskmaster environment is novel in that it

(provides an interactive, visual-based approach to user task specification;

(encourages and supports task specification and refinement processes from both the top-down and bottom-up perspectives; and

(enables one to specify parallel tasks in a natural and convenient manner

To “program” a given task within the Taskmaster environment, one decomposes it into an ordered set of conceptually simple, high-level operations, and then combines (composes) a corresponding network of software tools that implements these operations. Execution of the specified network provides a task solution. Major system components supporting user task specification include a network editor, a tools database and a network execution monitor.     

基于BCPNN法的药品不良反应信号检测与自动预警技术研究*

针对目前我国药品不良反应(ADR)信号检测与自动预警存在的问题,在分析当前各种ADR信号检测方法的基础上,研究并建立了基于BCPNN法和SRS数据库的ADR信号检测算法模型,应用Java语言开发了药品不良反应信号检测与自动预警程序,最后通过应用实例说明了上述方法的有效性。