基于黎曼空间的脑电信号特征提取和分类算法的对比研究

目前运动想象脑电信号(motor imagery electroencephalogram, MI-EEG)的分类方法主要分为两种,一种是利用人工设计MI-EEG特征的相似度/非相似度度量进行分类的算法,另一种是利用深度学习自动学习特征完成分类的算法。为探究两种方法的优劣及适用场景,本研究首先基于黎曼空间提出利用Stein散度作为MI-EEG的相似度/非相似度度量,用K最邻近法进行分类的算法;其次,提出利用黎曼流形结构下的卷积神经网络自动提取脑电信号特征进行分类的算法,最后对两种分类算法进行对比研究。为验证两种算法的有效性,在BCI Competition IV-2a公开数据集上进行实验测试。结果证明,两种分类算法均具有较强的稳定性和分类准确率,利用黎曼流形结构的卷积神经网络算法可获得更高的分类准确率,传统机器学习中利用Stein散度作为MI-EEG相似度/非相似度度量的脑电分类算法运行时间更短,更适合MI-EEG的在线解码。     

机器学习在抑郁症辅助诊断中的应用进展

随着抑郁症诊疗技术的发展,各种抑郁症相关的临床数据量急速扩增,机器学习技术恰好适用于大数量、多维度、多模态的数据,通过机器学习技术自动学习抑郁症诊疗数据中的特征,利用数据特征对抑郁症进行疾病诊断、疗效预测,达到抑郁症辅助诊断的目的。本文从机器学习在不同种类临床数据上应用的角度对文献进行了系统性分析,总结了机器学习在抑郁症辅助诊断领域的通用研究流程及常用研究方法,并展望未来的研究方向以及面临的挑战。     

药物功能模式相似度及其聚类

对现有已知药物的功能模式进行分析,可以帮助发现其可能的新应用,指导联合用药或预测药物的未知毒副作用.提出将药物化学结构信息和GO注释信息结合,分析药物功能模式相似度.药物化学结构和GO注释信息下载自DrugBank数据库,其中GO注释信息包括生物过程、分子功能和细胞定位等3个分支.计算现有4886种药物的功能模式相似度,并对其进行聚类分析.基于Tanimoto系数计算药物化学结构相似度,基于语义分析计算药物GO注释中3个分支的相似度.分别使用Logistic回归、算术均值、几何均值将上述4个药物相似度结合,得到反映多方信息的复合相似度.将一种药物与所有其他药物的相似度向量作为该药物的特征谱,对药物进行层次聚类.使用药物解剖学、治疗学及化学分类( ATC)的标准评价不同的相似度和聚类结果.结果显示:药物化学结构相似度与基于GO的3个分支的相似度均线性相关,表明药物的结构信息能在一定程度上反映功能信息;Logistic回归复合相似度能够很好地反映两个药物是否属于同一个ATC分类;基于GO注释生物过程分支语义相似度和几何均数复合相似度聚类结果与ATC分类第一层次强关联.所提出的方法结果可靠,可望用于辅助药物发现和预测不良反应.     

基于SEER数据库利用机器学习方法分析乳腺癌的预后因素

目的以SEER数据库中1990—2014年间的乳腺癌数据为研究对象,利用机器学习方法,分析乳腺癌的预后因素,辅助医师对患者的预后进行有效评判。方法根据临床医师的建议,筛选了12个字段作为模型输入字段,以术后5年生存状况作为模型输出字段。首先利用单因素统计分析方法初步筛选预后因素,再分别利用logistic回归和决策树两种机器学习分类算法进行建模分析,藉此寻找影响乳腺癌5年预后的因素。采用十折交叉法组织样本数据,并利用过抽样和欠抽样技术进行样本的平衡处理;以灵敏度、特异度及ROC下的AUC等参数作为模型的评价指标。结果在12个模型输入字段中,肿瘤分期、肿瘤分级、肿瘤尺寸、雌激素水平、年龄分组、孕激素水平等因素对于乳腺肿瘤预后具有较大影响;在此两种模型下,模型测试集上的灵敏度和特异度均介于74.2%~78.2%之间,AUC均处于0.838~0.850之间。结论利用Logistic回归和决策树算法构建乳腺癌患者的优化预后模型,可辅助医师判断患者预后情况及治疗效果。     

以肺痨病为例探讨中医证候本体的构建

本体是语义web的关键技术。中医证候本体的构建对于中医理论探讨、数据整理、知识共享是一件极具现实和学术意义的工作。本文探讨了中医哲学对中医证候本体的影响，阐明了中医证候本体与中医顶层本体的关系；并以protege-owl plugin为工具．对中医证候本体的构建思路和过程进行简单的介绍。     

基于机器学习的肝癌无创检测

目的根据肝癌临床诊断的需求,建立肝癌诊断预测模型,以达到无创检测肝癌的目的。方法利用德国企业产ILD3000型电子鼻设备采集正常受试者和肝癌患者的呼气数据,对呼气所得时间序列数据进行特征提取,包括序列数据的最大值、最小值、均值、标准差、序列数据总和等统计学特征。结合特征降维算法和机器学习分类模型对呼气特征数据进行正常受试者和原发性肝癌患者的二分类实验。结果通过模型选择和参数调整,在线性核函数支持向量机上对呼气数据取得92.3%的最优二分类结果。结论以正常受试者和肝癌患者的呼气数据为样本,利用机器学习建模的方法可以对肝癌做出诊断预测,且在此数据上,线性核函数支持向量机算法具有最好的分类效果。     

基于元数据的异构蛋白质-蛋白质相互作用数据库整合

研究蛋白质-蛋白质相互作用是理解生命活动的基础。在蛋白质-蛋白质相互作用的研究过程中,产生了大量来源于实验和预测的数据。这些数据存储于彼此异构的数据库中。对上述异构数据库进行数据整合是实现共享和最大限度利用已有蛋白质-蛋白质相互作用数据必须解决的关键问题。据此问题提出了基于元数据理论和查询转换方法的异构数据库整合方案,并构建了一个基于网络的蛋白质-蛋白质相互作用相关异构数据库的整合平台,成功实现了对9个蛋白质-蛋白质相互作用数据库的整合。     

基于医学本体的临床路径知识库模型建设

目的建立临床路径知识库模型,提高临床路径软件的自适应性,提升电子病历应用水平。方法利用本体知识库编辑工具,从医院已有的临床路径病种数据应用出发,对临床知识库内容进行规范化研究和标准化表达,形成临床路径知识库模型。结果建立了58个病种的临床路径知识库,该知识库描述了医院临床路径内容的各个方面,可应用于新一代临床信息系统建设。结论临床路径本体知识库对智能化电子病历应用和临床决策支持系统有重要作用,有利于医院信息系统的语义集成,是新一代电子病历应用的重要组成部分。     

一种多特征融合的Web医学信息语义关系抽取方法

目的为给用户提供更为相关、整体和结构化的Web医学信息,提出一种多特征融合的语义关系抽取方法,以解决中文Web医学信息中两两医学实体之间语义关系的抽取。方法首先在混合句法分析算法的基础上构造包含词项、语义、词性、交互词、实体对距离、实体类别以及最短依赖关系特征的特征向量并结合支持向量机实现。对Web医学信息中师徒关系、擅长关系及从属关系抽取实验,比较在不同句法分析下、不同特征作用及不同机器学习算法下的语义关系抽取效果。结果从F估计和算法运行时间来看,混合句法分析下效果最佳。随着特征的加入,抽取效果不断提升,最后,对三类语义关系抽取最终获得81.16%、95.94%和86.16%的F估计值。结论基于多特征融合的语义关系抽取方法对于Web医学信息语义关系的抽取具有很好的效果。     

莲子心提取物毛细管电泳指纹图谱研究

目的建立中药材莲子心提取物毛细管电泳（HPCE）指纹图谱。 方法制备莲心碱对照品溶液和10批莲子心提取物供试品溶液。采用毛细管区带电泳模式压力进样,分析10批莲子心提取物供试品所有的峰数、峰值和峰位等参数以及色谱图,建立HPCE指纹图谱,并利用“中药指纹图谱相似度评价系统（2004A）”软件分析其指纹图谱的相似度。 结果 选定15个共有峰作为构成莲子心提取物指纹图谱稳定的特征峰,建立HPCE指纹图谱。相似度评价分析结果表明10批莲子心提取物HPCE指纹图谱相似度均在0．900～1．000之间,表明其指纹图谱整体面貌基本一致。 结论 建立的莲子心提取物HPCE指纹图谱稳定、可靠,可作为莲子心提取物的特征指纹图谱,为莲子心提取物的定性鉴别及内在质量评价提供了新的参考依据。     

Integration of prostate cancer clinical data using an ontology

It is increasingly important for investigators to efficiently and effectively access, interpret, and analyze the data from diverse biological, literature, and annotation sources in a unified way. The heterogeneity of biomedical data and the lack of metadata are the primary sources of the difficulty for integration, presenting major challenges to effective search and retrieval of the information. As a proof of concept, the Prostate Cancer Ontology (PCO) is created for the development of the Prostate Cancer Information System (PCIS). PCIS is applied to demonstrate how the ontology is utilized to solve the semantic heterogeneity problem from the integration of two prostate cancer related database systems at the Fox Chase Cancer Center. As the results of the integration process, the semantic query language SPARQL is applied to perform the integrated queries across the two database systems based on PCO.     

Quality evaluation of cancer study Common Data Elements using the UMLS Semantic Network

The binding of controlled terminology has been regarded as important for standardization of Common Data Elements (CDEs) in cancer research. However, the potential of such binding has not yet been fully explored, especially its quality assurance aspect. The objective of this study is to explore whether there is a relationship between terminological annotations and the UMLS Semantic Network (SN) that can be exploited to improve those annotations. We profiled the terminological concepts associated with the standard structure of the CDEs of the NCI Cancer Data Standards Repository (caDSR) using the UMLS SN. We processed 17798 data elements and extracted 17526 primary object class/property concept pairs. We identified dominant semantic types for the categories "object class" and "property" and determined that the preponderance of the instances were disjoint (i.e. the intersection of semantic types between the two categories is empty). We then performed a preliminary evaluation on the data elements whose asserted primary object class/property concept pairs conflict with this observation - where the semantic type of the object class fell into a SN category typically used by property or visa-versa. In conclusion, the UMLS SN based profiling approach is feasible for the quality assurance and accessibility of the cancer study CDEs. This approach could provide useful insight about how to build mechanisms of quality assurance in a meta-data repository.     

Evaluating semantic similarity between Chinese biomedical terms through multiple ontologies with score normalization: An initial study

BackgroundSemantic similarity estimation significantly promotes the understanding of natural language resources and supports medical decision making. Previous studies have investigated semantic similarity and relatedness estimation between biomedical terms through resources in English, such as SNOMED-CT or UMLS. However, very limited studies focused on the Chinese language, and technology on natural language processing and text mining of medical documents in China is urgently needed. Due to the lack of a complete and publicly available biomedical ontology in China, we only have access to several modest-sized ontologies with no overlaps. Although all these ontologies do not constitute a complete coverage of biomedicine, their coverage of their respective domains is acceptable. In this paper, semantic similarity estimations between Chinese biomedical terms using these multiple non-overlapping ontologies were explored as an initial study.MethodsTypical path-based and information content (IC)-based similarity measures were applied on these ontologies. From the analysis of the computed similarity scores, heterogeneity in the statistical distributions of scores derived from multiple ontologies was discovered. This heterogeneity hampers the comparability of scores and the overall accuracy of similarity estimation. This problem was addressed through a novel language-independent method by combining semantic similarity estimation and score normalization. A reference standard was also created in this study.ResultsCompared with the existing task-independent normalization methods, the newly developed method exhibited superior performance on most IC-based similarity measures. The accuracy of semantic similarity estimation was enhanced through score normalization. This enhancement resulted from the mitigation of heterogeneity in the similarity scores derived from multiple ontologies.ConclusionWe demonstrated the potential necessity of score normalization when estimating semantic similarity using ontology-based measures. The results of this study can also be extended to other language systems to implement semantic similarity estimation in biomedicine.     

User-centered semantic harmonization: a case study

Semantic interoperability is one of the great challenges in biomedical informatics. Methods such as ontology alignment or use of metadata neither scale nor fundamentally alleviate semantic heterogeneity among information sources. In the context of the Cancer Biomedical Informatics Grid program, the Biomedical Research Integrated Domain Group (BRIDG) has been making an ambitious effort to harmonize existing information models for clinical research from a variety of sources and modeling agreed-upon semantics shared by the technical harmonization committee and the developers of these models. This paper provides some observations on this user-centered semantic harmonization effort and its inherent technical and social challenges. The authors also compare BRIDG with related efforts to achieve semantic interoperability in healthcare, including UMLS, InterMed, the Semantic Web, and the Ontology for Biomedical Investigations initiative. The BRIDG project demonstrates the feasibility of user-centered collaborative domain modeling as an approach to semantic harmonization, but also highlights a number of technology gaps in support of collaborative semantic harmonization that remain to be filled.     

Using machine learning techniques and genomic/proteomic information from known databases for defining relevant features for PPI classification

In modern proteomics, prediction of protein-protein interactions (PPIs) is a key research line, as these interactions take part in most essential biological processes. In this paper, a new approach is proposed to PPI data classification based on the extraction of genomic and proteomic information from well-known databases and the incorporation of semantic measures. This approach is carried out through the application of data mining techniques and provides very accurate models with high levels of sensitivity and specificity in the classification of PPIs. The well-known support vector machine paradigm is used to learn the models, which will also return a new confidence score which may help expert researchers to filter out and validate new external PPIs. One of the most-widely analyzed organisms, yeast, will be studied. We processed a very high-confidence dataset by extracting up to 26 specific features obtained from the chosen databases, half of them calculated using two new similarity measures proposed in this paper. Then, by applying a filter-wrapper algorithm for feature selection, we obtained a final set composed of the eight most relevant features for predicting PPIs, which was validated by a ROC analysis. The prediction capability of the support vector machine model using these eight features was tested through the evaluation of the predictions obtained in a set of external experimental, computational, and literature-collected datasets.     

A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains

In order to enable secondary use of Electronic Health Records (EHRs) by bridging the interoperability gap between clinical care and research domains, in this paper, a unified methodology and the supporting framework is introduced which brings together the power of metadata registries (MDR) and semantic web technologies. We introduce a federated semantic metadata registry framework by extending the ISO/IEC 11179 standard, and enable integration of data element registries through Linked Open Data (LOD) principles where each Common Data Element (CDE) can be uniquely referenced, queried and processed to enable the syntactic and semantic interoperability. Each CDE and their components are maintained as LOD resources enabling semantic links with other CDEs, terminology systems and with implementation dependent content models; hence facilitating semantic search, much effective reuse and semantic interoperability across different application domains. There are several important efforts addressing the semantic interoperability in healthcare domain such as IHE DEX profile proposal, CDISC SHARE and CDISC2RDF. Our architecture complements these by providing a framework to interlink existing data element registries and repositories for multiplying their potential for semantic interoperability to a greater extent. Open source implementation of the federated semantic MDR framework presented in this paper is the core of the semantic interoperability layer of the SALUS project which enables the execution of the post marketing safety analysis studies on top of existing EHR systems.     

The neural signature of spatial frequency-based information integration in scene perception

Spatial frequency-based information plays an important role in visual perception. By combining behavioral and electroencephalogram (EEG) measurements, we investigated the mechanisms of the interaction and information integration between different spatial frequency bands. The observers performed a scene categorization task on hybrid images that were generated by combining the low spatial frequency (LSF) component of one image with the high spatial frequency (HSF) component of another image. The results showed that the recognition of the HSF component was interfered by the non-attended LSF component at semantic level. The strength of the semantic interference was modulated by the physical similarity between the LSF and HSF components. Analyses of the EEG data revealed an early anterior N1 component (122 ms from stimulus onset) that was related to the observed interaction of the semantic and physical information between the LSF and HSF components. These findings demonstrate that the semantic information from different spatial frequency bands can be integrated at early stage of the perceptual processing. This early integration is likely to occur at frontal areas in order to initiate top-down facilitation.     

Managing Biomedical Image Metadata for Search and Retrieval of Similar Images

Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations (“semantic” metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system’s “match observations” function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.     

A new method of content based medical image retrieval and its applications to CT imaging sign retrieval

This paper proposes a new method of content based medical image retrieval through considering fused, context-sensitive similarity. Firstly, we fuse the semantic and visual similarities between the query image and each image in the database as their pairwise similarities. Then, we construct a weighted graph whose nodes represent the images and edges measure their pairwise similarities. By using the shortest path algorithm over the weighted graph, we obtain a new similarity measure, context-sensitive similarity measure, between the query image and each database image to complete the retrieval process. Actually, we use the fused pairwise similarity to narrow down the semantic gap for obtaining a more accurate pairwise similarity measure, and spread it on the intrinsic data manifold to achieve the context-sensitive similarity for a better retrieval performance. The proposed method has been evaluated on the retrieval of the Common CT Imaging Signs of Lung Diseases (CISLs) and achieved not only better retrieval results but also the satisfactory computation efficiency.