基于医学信息数据仓库模型的数据挖掘

利用数据仓库和数据挖掘技术,以现有医院信息系统HIS及医学信息资源为基础,基于PC和Windows操作系统,利用SQL Server2005及SQL Server 2005 Analysis Services（SSAS）等软件,搭建了医学信息数据仓库模型,并运用数据挖掘技术抽取数据库中数据隐藏的规律,提高医学信息的利用率。为从错综复杂的、庞大的医学信息库中提取有价值的决策支持信息提供有效的途径和方法。     

医学信息的虚拟存储及其系统模型的构建

本文引入了医学信息的虚拟存储新理念,用以解决海量医学数据的存储难题,并提出了在上海广电集团所建立的上海远程医学网络平台上的具体设计方案,包括建立虚拟映射,数据访问中数据传输技术的研究以及存储管理的设想。     

面向药品决策支持系统的数据仓库模型设计

医院药品的业务庞杂,数据集成与分析不完善,缺少信息全面、集成的数据仓库系统。研究医院药品数据,运用“业务维度生命周期法”进行数据仓库项目的设计、开发和部署。解决的问题有：创建数据仓库总线结构,建立主题模型,使用维度建模来进行逻辑建模,数据存储的物理设计,数据转储与开发。总体逻辑结构模型设计清晰,构建方法新颖,给出一个较好的医院药品数据仓库的分析模型。     

医学信息的远距离传输

一、引言 由于当代生物医学与电子——信息科学突飞猛进的发展,以及全球医疗水平在国家之间、地区间和城乡间的差距日益扩大,科学家正在探索采用各种先进的通信网络来实现远程医疗,用来将发达地区的先进医疗技术快速地输送到偏远、落后地区,以解决人类自身对医疗保健的迫切需要。同时先进的通信网络也为生命科学研究及医学临床实践间建立了学术交流、远程诊断与监护、急救、会诊、     

基于物候特征的盐渍化信息数据挖掘研究

盐渍化是影响植被和作物长势的重要因素,精确反演盐渍化的时空分布信息至关重要。基于MOD13A1-NDVI数据反演生长季开始日期(SOS)、生长季结束日期(EOS)、生长季长度(LEN)等物候参数和计算出能高精度反演盐渍化空间分布的多种植被指数、盐分指数、地形指数、干旱指数等参数后作为BP-ANN人工神经网络的输入因子来反演盐渍化信息,同时按照植被类型和地貌类型进行分区来反演盐渍化信息,以探讨盐渍化受植被和地貌类型的影响。主要结论如下:(1)盐渍化的形成受多种因素的影响,与物候参数大多呈非线性关系,不能单纯的以某拟合公式来进行表达,需要借助人工神经网络超强的非线性拟合能力来反演盐渍化信息。(2)通过深入挖掘植被物候信息,在融入物候参数后的反演精度显著提高。可决系数R2从0.68(非物候参数)增加到0.79(包括物候参数),但是需要加入地形、影像数据和土壤水分等方面的信息来更加精确的反演盐渍化信息。生物累积量指标LSI(Large seasonal integral)和SSI(Small seasonal integral)能够很好的表征盐渍化的信息。(3)划分植被类型后的盐渍化提取精度进一步提高,可决系数R~2达到了0.88。(4)以地貌特征作为类型分区后,反演结果的R~2达到了0.85,精度较高,比以植被类型作为分区的精度略小。高程较低区域的盐渍化现象普遍较重,盐渍化程度受到地形和地貌因素的影响显著。(5)农用地区域多为非盐渍化和轻度盐渍化地,稀疏植被区多为重盐渍化地。研究区的非盐渍化和轻盐渍化地、中盐渍化地和重度盐渍化地比例分别为53.42%,13.71%,32.87%。以上的研究结果提出了一种融合物候信息和非物候参数来反演盐渍化信息的方法,进行深入的协同植被物候监测盐渍化信息方面的数据挖掘,在融入了物候参数后,盐渍化的预测精度显著提高。     

加入WTO对医学信息服务的影响

探讨了我国加入WTO后给医学图书信息事业带来的挑战和机遇,提出了振兴事业的策略。     

加入WTO对医学信息服务的影响

探讨了我国加入WTO后给医学图书信息事业带来的挑战和机遇,提出了振兴事业的策略。     

医学图书馆的网络信息资源利用

网络信息日益深入我们的生活,为我们获取知识提供了极大的便利,现实表明医学图书馆网络信息资源的开发与利用,图书资源与网络信息资源结合的优势组合,已极大地拓展广大医务工作者及相关人员的知识领域.如何借助信息技术,依托网络环境,转变单一的馆藏发展模式,选取网上信息资源拓展馆藏,更深入地开发网上信息资源供读者利用是本文论述的目的.     

基于网格的医学信息平台设计

针对目前医学信息应用模式的局限性,提出一种基于网格的平台技术,促进网络环境下的医学资源共享和互用。其中采用面向网格工具包的中间件设计,简化了服务集成和调用。实验模型的建立验证平台的可行性及实用价值。     

人工智能大模型医学应用研究

近年来,以自然语言处理和视频图像分析为主的人工智能大模型技术得到快速发展,其基本特征是聚焦相关应用领域的共性需求,通过大数据、强算力和复杂算法的高效协同与深度融合,构建通用预训练模型,广泛适配下游任务,有力提高模型的处理性能与研发效率.因此,大模型技术为医学人工智能高质量发展提供了难得契机.本文通过全面梳理国内外大模型的研究进展、关键技术与核心算法,分析总结生物医学领域一系列标准数据集和预训练模型的发展特点,结合医学人工智能的研发实践,深入剖析医学领域大模型构建的应用需求、解决思路与研发经验,助力推动医学大模型创新发展.     

数据挖掘在蛋白质组学研究中的应用

介绍蛋白质组学研究的背景;阐述数据挖掘的原理、方法,重点讲述数据挖掘技术在蛋白质组学研究中取得的新的进展。最后,对数据挖掘目前存在的问题作分析,并对它的发展的前景作展望。     

基于数据挖掘的门诊信息资源分析

目的：随着各级医院信息化建设的不断加强,医院的信息化水平也日益提高。目前各医院都有自己完善的信息化系统,在日常的门诊中,信息化系统积累了大量的门诊就诊数据,但长久以来这部分数据只是处于低层次的应用。对数据的深层次分析、加工以及对医院管理层的决策支持能力较弱。面对着这些宝贵的数据,医院迫切需要数据挖掘和分析工具从积累的就诊数据中分析出更深层次的、高价值的信息,从而为医院的管理决策提供高价值的决策信息。方法：以聚类算法进行数据挖掘建模,对某院门诊信息资源中有用字段进行挖掘分析。结果：根据数据挖掘模型进行挖掘分析,对有价值字段进行聚类分析,得到相关字段数据挖掘结果。结论：将得到相关字段数据挖掘结果进行分析,并将所分析的结果在医院管理决策和医疗质量管理等方面的应用进行探讨。     

From Data towards Knowledge: Revealing the Architecture of Signaling Systems by Unifying Knowledge Mining and Data Mining of Systematic Perturbation Data

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal; and 3) revealing the architecture of a signaling system by organizing signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis has led to many new hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architecture of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which can be readily translated into computable knowledge in the form of rules regarding the yeast signaling system, such as “if genes involved in the MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed.”     

基于决策树的红色籽用西瓜数据挖掘

利用Clementine软件提供的决策树C5．0算法,探索红色籽用西瓜自交系多个数量性状之间的相互制约关系。初步建立了单瓜种子重决策树模型,预测精度达到68％。可据此模型对红籽瓜自交系的各经济性状进行定量分析,为育种生产者进行优良自交系和亲本选配提供决策。     

香菇多糖的生物活性

香菇多糖 (ｌｅｎｔｉｎａｎ ,ＬＮＴ)是从伞菌科真菌香菇(ｌｅｎｔｉｎｕｓｅｄｏｄｅｓ)的子实体中分离到的一种 β 1,3 葡聚糖 ,2 0世纪 6 0年代日本科学家首先证明其具有显著的免疫调节活性和抗肿瘤活性 ,经临床验证 ,已在国际市场上推广应用。1.香菇多糖免疫调节活性香菇多糖的免疫调节活性是其生物活性的重要基础。香菇多糖是典型的Ｔ细胞激活剂 ,体内外均能促进细胞毒Ｔ淋巴细胞 (ＣＴＬ)的产生 ,提高ＣＴＬ的杀伤活力 ,增强正常或免疫功能低下小鼠的迟发型超敏反应 (ＤＴＨ) ,提高抗体依赖性细胞毒细胞(ＡＤＤＣ)活性。香菇多糖在体…     

Drug Repositioning for Diabetes Based on 'Omics' Data Mining

Drug repositioning has shorter developmental time, lower cost and less safety risk than traditional drug development process. The current study aims to repurpose marketed drugs and clinical candidates for new indications in diabetes treatment by mining clinical ‘omics’ data. We analyzed data from genome wide association studies (GWAS), proteomics and metabolomics studies and revealed a total of 992 proteins as potential anti-diabetic targets in human. Information on the drugs that target these 992 proteins was retrieved from the Therapeutic Target Database (TTD) and 108 of these proteins are drug targets with drug projects information. Research and preclinical drug targets were excluded and 35 of the 108 proteins were selected as druggable proteins. Among them, five proteins were known targets for treating diabetes. Based on the pathogenesis knowledge gathered from the OMIM and PubMed databases, 12 protein targets of 58 drugs were found to have a new indication for treating diabetes. CMap (connectivity map) was used to compare the gene expression patterns of cells treated by these 58 drugs and that of cells treated by known anti-diabetic drugs or diabetes risk causing compounds. As a result, 9 drugs were found to have the potential to treat diabetes. Among the 9 drugs, 4 drugs (diflunisal, nabumetone, niflumic acid and valdecoxib) targeting COX2 (prostaglandin G/H synthase 2) were repurposed for treating type 1 diabetes, and 2 drugs (phenoxybenzamine and idazoxan) targeting ADRA2A (Alpha-2A adrenergic receptor) had a new indication for treating type 2 diabetes. These findings indicated that ‘omics’ data mining based drug repositioning is a potentially powerful tool to discover novel anti-diabetic indications from marketed drugs and clinical candidates. Furthermore, the results of our study could be related to other disorders, such as Alzheimer’s disease.     

乳腺癌基因芯片实验数据分析与挖掘

目的 对公共数据库上下载得到的乳腺癌基因芯片试验结果进行数据分析,找出在正常组织与癌组织中呈现差异表达的基因,并寻找差异表达基因的相关基因.方法 综合运用显著性分析（SAM）、顶级评分基因对（TSP）、关联规则挖掘等方法,对数据进行处理.结果 筛选出若干呈现差异表达的基因,并且寻找了其中一部分基因的可能高度相关的基因.结论 筛选出的基因及其相关基因可用于为进一步的研究提供候选基因.     

Data Mining Strategies to Improve Multiplex Microbead Immunoassay Tolerance in a Mouse Model of Infectious Diseases

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.