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Semi-automatic data migration in a self-medication Knowledge-based system

Semi-automatic data migration in a self-medication Knowledge-based system
Semi-automatic data migration in a self-medication Knowledge-based system

Semi-automatic data migration in a self-medication

Knowledge-based system

Olivier Curé

ISIS Laboratory, Cité Descartes – 5, bld Descartes

Champs-sur-Marne – 77454 Marne-la-Vallée Cedex 2 – France

ocure@univ-mlv.fr

Abstract. Self-medication, defined as the act to treat oneself with or without

drugs,is a common practice in industrial countries.A study of available

computerized solutions in this field highlights that this issue has not been

considered with enough attention, although they provide valuable services to

both patients and health care organizations. This paper presents XIMSA, a

self-medication knowledge-based system, which is supported by a database/

ontology collaboration.This collaboration is guaranteed by DBOM,an

application-independent system which enables the end-user to design, enrich

and maintain an ontology from an existing database. DBOM's functionalities

are presented within XIMSA's application domain.

1Introduction

In order to become profitable and enterprise-like structures, health care organizations (henceforth HCO) need to provide services to all involved actors. Usually HCOs provide a large attention to health care professionals (physicians, pharmacists, etc.) but rarely concentrate their efforts on patients. Most of the time, this leads the patient to a semantic isolation whenever he is confronted with medical information, data and knowledge.

Our collaboration with the clinical pharmacology department at the Cochin hospital in Paris (France) has resulted in the implementation of IMSA (Interactive Multimedia for Auto-medication)[6].This Knowledge-Based System(henceforth KBS) aims at providing information and services to the general-public on mild clinical signs, related treatments and medications. The latest version of this system, XIMSA (eXtended IMSA) bundles together a drug and symptom database, a self-medication ontology, a simplified patient electronic health record and an inference engine.The results provided by the inference engine depend on the ontology/database collaboration efficiency,which is undertaken by DBOM (DataBase Ontology Mapping), a domain independent application providing data integration and maintenance services in a Semantic Web environment [5].

This paper is organized as follows : section 2 presents the main characteristics of self-medication,section3focuses on XIMSA's architecture and functionalities, section 4 proposes on overview of DBOM, section 5 emphasizes a database/ontology

collaboration, section6 concludes with a discussion on future extensions of the XIMSA and DBOM systems.

2Self-medication

Self-medication can be defined as the health activities to treat oneself with or without drugs. People self-medicate using information obtained from past health experiences, books, advices, software, web sites, health advertising, radio or TV programs. On the medication side, people usually self prescribe drugs they already have at home and buy Over The Counter(OTC)products.These products are unevenly distributed over therapeutic classes (respiratory and digestive systems drugs are the most self-prescribed).

Self-medication is popular in most industrial countries, e.g.a recent study estimated that 91% of French citizens self-prescribe drugs when confronted to a known symptom [1]. The act to self-medicate is also an interesting financial market which represented 9.7% (2 billion euros) of the global pharmaceutical market for 1999 in France [1]. The French government, in its struggle with the French social security system deficit (14 billion euros for 2003), is indirectly encouraging self-medication through a series of actions : drug switch, lower the reimbursement rate for some drugs, favoring the emergence of the generics market, etc..

HCOs are as much aware of the semantic isolation of most patients as they are aware of the increasing success of self-medication. A logical correlation between these facts partially explain some alarming French figures for2004:128,000 hospitalizations due to drug interactions and 10,000 deaths due to drug over and mis-consumption.

The current policy of the French government is to encourage patients to become responsible and (pro)active health actors, but at the same time not much support, in terms of guidelines, books and computer tools, are proposed. Officially, the French healthcare system relies on its physician and pharmacist network to provide information on safe practice of self-medication.We believe that KBS is an alternative that has not been exploited with the proper attention. Such solutions may benefit from rapidly emerging markets,such as high speed Internet access and Internet compliant mobile phones, to reach an important portion of the population.

3XIMSA

The XIMSA web application proposes self-medication services to the general public and aims to make this health care act a safer one and to free the patient from the semantic isolation related to medical information.

XIMSA' architecture is based on 4 distinctive modules (module interactions are presented in figure 1) :

–the XIMSA database stores symptom and drug data related to self-medication,

–the XIMSA ontology uses the OWL [8] syntax to represent terminological and assertional knowledge in a self-medication context.,

–the Simplified Electronic Health Record (SEHR) stores information (in an XML syntax) concerning data such as clinical antecedents and the history of drug consumption for a particular patient,

–the inference engine makes deduction with respect to the XIMSA ontology, the patient's SEHR and the data acquired during the navigation within XIMSA [6].

Figure 1 : Interaction-based architecture of XIMSA

The advantages provided by XIMSA's functionalities can be assessed in two categories. The first category is concerned with the quality of treatment which is ensured by :

–the confidence that the system provides advices and drug propositions only for mild clinical signs that can be treated via self-medication,

–the adequacy between the symptom described by the patient and the therapeutic classes proposed,

–the adaptability and accuracy of the drugs provided for a treatment, with respect to the clinical and pharmacological information stored in the SEHR of the patient,–the value of the drugs presented based on an efficiency/tolerance ratio rating,

–the usage of such systems will increase the overall knowledge of end-users and will improve the communication between patients and healthcare professionals.

The second category is related to the controlling of costs :

–on the patient side, the system provides a direct access to OTC drug prices.

Although not reimbursed by the Social Security system, these drugs may cost less to the patients due to their lower prices compared to prescription(partially reimbursed) drugs,

–on the Social Security system side, avoidance of reimbursement are guaranteed on the physician consultation and the drugs proposed.

–Finally, for both patients and the Social Security system, a global visibility of drug prices over all therapeutic classes may encourage the usage of the less expensive generics drugs.

4DBOM

XIMSA's effectiveness is based on the quality and accuracy of the self-medication ontology and the SEHR. Although the patient/end-user is solely responsible for the value of the SEHR, the ontology's quality is undertaken by DBOM. An important part of this ontology focuses on drug related data, a field with a high update rate which requires storage in a database. Starting from the fact that "databases are similar to knowledge base because they are usually used to maintain models of some domain of discourse"[3], the idea of DBOM is to tackle the problem of database-to-ontology mapping.

This problem has been addressed by several research groups but DBOM's approach is more concerned with the following issues : data storage redundancy and inference efficiency. The consideration of these issues is done at the price of a non-automatic ontology design. For example, in [9] and [4], the goal is to (fully) automate data migration by transforming the relational database model into corresponding ontological structures,while[2]'s architecture is based on an existing ontology structure (non-automatic).

The approach adopted by DBOM is semi-automatic and involves the end-user to select amongst database components (relations, attributes, keys) which are going to map to the ontology structures. This solution ensures that the ontology will not contain concepts, properties and instances unnecessary to the inference engine. The vision of the DBOM system is to develop applications that use the ontology for inference purposes and is able to bind the inference results to the database thus providing valuable information to the end-user.In order to reach this goal,the designer of the mapping must be aware of the database schema and needs a clear vision of the characteristics of the implemented application, including inferences.

A high potential of the DBOM framework is to design domain and application ontologies [7] from existing databases. An important fact about databases are that update operations may also update the domain of discourse.Thus the designed ontology requires to be synchronized with the database. The proposed system offers such features and extends them to permit symmetrical maintenance solutions, meaning that controls are done both ways : from database to the ontology (ontology updating, e.g. adding new instances) and from the ontology to the database (e.g. consistency checking).

The main motivation behind the maintenance features remains in the database / ontology systems separation. This separation requires that the database schema is not modified during mapping processing and enables users of the DBOM framework to benefit from database features which are not available in ontology engineering (concurrency control, transaction, crash recovery, advanced storage techniques and query languages) as well as features of OWL ontologies, and underlying Description Logics properties and functionalities [3].

The description of the DBOM framework is divided into four distinct components: design, enrichment, ontology and database maintenances (see figure 2).

Fig. 2 : Global vision of interactions with the DBOM framework.

The design component supports the creation of the TBox (a set of terminological axioms) in an OWL syntax. This phase is partially done by the end-user who uses his knowledge of the database schema to describe a mapping file (an XML document) to the ontology. This description consists in describing the database relations, attributes and possible conditions implied in the design of the ontology. The approach adopted enables DBOM to generate hierarchies of complex classes and properties in the Tbox.

The enrichment component deals with instantiating the Abox (a set of assertional axioms) with individuals obtained from the database. The performances of this phase are increased due to the storage of SQL queries in the mapping file. A simple parsing of a mapping file enables queries to be executed in the database and thus creates new assertions in the ontology.

The maintenance components are dealing with ABox updates and database consistency checking.The ontology maintenance phase is concerned with(non-schema) updates of the databases, e.g. insertion of a new tuple in the database fires a trigger that may creates a corresponding instance in the ontology. The enrichment phase is usually performed once for an ontology schema while maintenance may be processed several times during the life cycle of a terminology. The last component of DBOM is related to the database maintenance which is ensured by consistency checking of an ABox w.r.t. a TBox. This database maintenance is executed after an effective, at least one trigger has been fired, ontology maintenance. The objective of this maintenance is to ensure that characteristics of new instances are consistent with

the semantics of the ontology, something the DBMS can not process due to its lack of detailed semantics. The current philosophy of the database maintenance is not to act directly on the database Thus the approach adopted is to propose a log file, in an XML format, to the end-user.

5Ontology/Database collaboration

The main features (design, enrichment and maintenances) of the DBOM application are used on the XIMSA system. An important part of XIMSA's self-medication ontology has been designed using DBOM based on a drug database which contains all drugs available in France. For each drug, the database regroups all the data of the Summary of Product Characteristics (SPC) plus extra information such as opinions from health care professionals and a drug rating.

The integration of the ontology in XIMSA enables the patient to control drug prescription regarding data contained in his SEHR which is created and maintained within the web-based XIMSA interface and enables the patient to store general (name, gender, date of birth, etc.) and health related (clinical and drug interactions, drugs being prescribed, etc.) information. An accurate and up-to-date SEHR assists the system in providing safe drug prescription to a specific patient. A ruled-based mechanism handles the ontology, the SEHR, and a particular request of the end-user (for example requiring an antitussive drug) to propose hyper links of safe to prescribe drugs.The result of the inference mechanisms provides identifiers of eligible ontology instances. Due to the correspondence between the ontology instances and the database tuples, the end-user can click on a hyper link and obtain all the information stored in the database about this drug,including information not contained in the ontology (e.g. drug price).

Finally, updates of the ontology produce a consistency checking. Lets consider the addition of a new drug in the database where the therapeutic class is not consistent with the Recommended International Non-proprietary Name (RINN, the active molecule) of the drug. Although the statement recorded in the database is valid, its semantic is wrong. The consistency checking of the ontology will report, in a log file, that an inconsistent statement has been added in the database. A study of the log file will enable the database administrator to change the therapeutic class of this drug.

6Discussion

The efficiency of XIMSA relies on the quality and quantity of information stored in the SEHR. If the end-user/patient inputs sufficient data concerning clinical (e.g. whether he is suffering from certain diseases)and pharmacological(current consumptions, allergies, etc..) aspects then valuable inferences are provided.

In order to ensure an accurate and up-to-date SEHR, health care professionals may become sources of information. Both patients and physicians would benefit from this collaboration : the physician would be aware of all drugs taken by the patient and the patient would be ensured to have a valid,accurate SEHR concerning non self-medication descriptions and treatments. On the HCO side,such a distributed

collaboration may increase the overall quality of self-medication with the possibility to study, understand and control this medical activity.

The DBOM application enables the design and maintenance of high quality ontologies by providing correctness and minimally redundant data. The correctness quality is provided by the capture of the intuitions of domain experts which is facilitated by a conceptual-concerned collaboration with the designer.This collaboration also benefits from a knowledge representation language abstraction and fast access to a realistic, richly instantiated ABox. The minimal redundancy quality is provided by the database/ontology separation considering that the ontology only contains relations and attributes concerned with inference mechanisms.

A study of DBOM also emphasizes economical aspects with the following facts : the database and the ontology can concurrently be accessed and maintained, the knowledge acquisition and updates are done at no extra costs, the guarantee that the system will be adopted by the experts because they were involved in the design of the ontology.

Although the XIMSA auto-medication ontology contains more than 6000 drug products, 1500 RINN and 500 therapeutic classes, we believe that studies with larger ontologies, meaning larger TBoxes and ABoxes, need to be conducted. Performance surveys should also be conducted with such ontologies. The DBOM framework also requires the implementation of a graphical QBE-like solution for the design of database to ontology mapping file.

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写作中,不可忽略的是钢琴伴奏织体的作用,因此作曲家们通常都以丰富的伴奏织体来烘托歌曲的意境,铺垫音乐背景,增强音乐感染力。和声织体,复调织体都在许多作品中使用,较为常见的是综合织体。这些不同的伴奏织体的歌曲,极大限度的发挥了钢琴的艺术表现力,起到了渲染歌曲氛围,揭示内心情感,塑造歌曲背景的重要作用。钢琴伴奏成为整体乐思不可缺少的部分。优秀的钢琴伴奏织体,对发掘歌曲内涵,表现音乐形象,构架诗词与音乐之间的桥梁等方面具有很大的意义。在不断发展和探索中,也将许多伴奏织体使用得非常娴熟精确。 二、青主艺术歌曲《我住长江头》中钢琴伴奏的特点 《我住长江头》原词模仿民歌风格,抒写一个女子怀念其爱人的深情。青主以清新悠远的音乐体现了原词的意境,而又别有寄寓。歌调悠长,但有别于民间的山歌小曲;句尾经常出现下行或向上的拖腔,听起来更接近于吟哦古诗的意味,却又比吟诗更具激情。钢琴伴奏以江水般流动的音型贯穿全曲,衬托着气息宽广的歌唱,象征着绵绵不断的情思。由于运用了自然调式的旋律与和声,显得自由舒畅,富于浪漫气息,并具有民族风味。最有新意的是,歌曲突破了“卜算子”词牌双调上、下两阕一般应取平行反复结构的惯例,而把下阕单独反复了三次,并且一次比一次激动,最后在全曲的高音区以ff结束。这样的处理突出了思念之情的真切和执著,并具有单纯的情歌所没有的昂奋力量。这是因为作者当年是大革命的参加者,正被反动派通缉,才不得不以破格的音乐处理,假借古代的

playapartin的用法

play a part in的用法 Do you know this pretty girl Right! She is Audrey Hepburn who played many classic roles in a great many famous films. For example, < Roman Holiday> is the one of the films which earned Hepburn her first Academy Award for Best Actress. Audrey Hepburn played the part/role of Princess Ann in this film. play the part/role of…扮演……角色

Audrey Hepburn played a leading part in directed by Steven Spielberg.( Always 直到永远/天长地久) play a part in…在……中扮演角色,在……中起作用

Audrey Hepburn also played leading roles in < Funny Face>and < My Fair Lady〉.( My Fair Lady 窈窕淑女影片讲述下层卖花女被语言学教授改造成优雅贵妇的故事,从头至尾洋溢着幽默和雅趣 .Funny Face 甜姐儿) Play a role in…在……中扮演角色,在……中起作用 play a part/role in doing sth. 在做某事方面起作用,参与做某事 We can?all?play?a?role/part?in?reducing?our?dependence?on?plastic, if we started to?take some small?steps?in?our?everyday?lives?to?be

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同声传译 同声传译又名即时传译,顾名思义,是指基本同步的语言翻译。翻译工作由翻译员负责完成,同声传译设备只是提供操作的界面。换言之,配备同声传译设备后,这些设备组成的系统并不能对语言执行自动翻译,必须由传译员翻译。 当说不同语言的人会面,或召开国际性会议时,由于双方或者多方语言无法沟通,必须由翻译员进行沟通。这时就需要一套同声传译设备,每位代表佩带一副接收器和耳机选听,不管发言的代表是何种语言,都基本有一个与之对应的翻译员作出指定语言的翻译,这就是同声翻译设备的作用。 同声传译系统的核心技术是多语种旁听信号的传输(分配、发送与接收),主要由有线与无线两种方案构成,而无线传输又分为电磁波方式和红外线方式两种。 图3为模拟传输方式: 图3

红外线传输技术 红外线传输由于具备安装简单、音质良好、保密性强等优点,普遍应用在同声传译的语言分配与传输领域。采用红外传输的同声传译系统基本上由以下器材或子系统组成。 图4所示为红外线同声传译的典型系统图 图4 表决系统 在会议中我们需要一套表决系统,在会前可以作为参会人员签到、在会中可以对问题进行投票和表决,体现了民主、公平、公正、科学的原则。 图5为带表决的会议单元: (桌面式)(嵌入式) 图5 图6为整个会议系统系统图:

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I SMART 智能审图及远程互动审图一体机 秦工是一位资深的高级工程师,参加工作20多年来,一向以严谨认真、兢兢业业,在建筑设计院享有极高的声望。在设计院里年轻人都流传着一个段子,意思是说做设计师太辛苦,每天的生活都塞满了枯燥的设计图纸,仰着头那是在看投影,平着看那是在看电脑,低着头那是在看图纸,侧着眼看那是在看秦工。 每次跟秦工开这个玩笑时,秦工总是笑笑表示理解:虽然说现在用电脑用投影机代替了从前的手工作图,效率要高得多。但遇到投影显示不清楚,或者远远看着投影幕布说不明白,尤其是投影幕前无法批注圈点和保存文档,还得从成堆的图纸中翻出来指出存在的问题,精力的耗费在所难免。负责异地工程设计项目时,频繁的出差耗费了大量的时间和精力,旅途劳累加上繁重的工作任务更是让人身心疲惫。特别是见到一些设计师年纪轻轻患上严重的颈椎病或视力严重减退感到特别痛心。 一次偶然的机会,秦工见到英特智讯公司的智能审图及远程互动审图产品,它创造性地解决了图纸高清晰度显示、大幅面图纸快速调用、在屏幕上直观缩小放大以及拖动等问题,而且能够在图纸上直接手写标注,修改等,一键启动远程数据会议系统不仅能为远程审图的各方提供流畅的音视频,还能通过共享图纸和实时显示对图纸的操作实现远程互动交流,特别适用于建筑图纸讨论与评审,就推荐到建筑设计院使用。经过简单的培训,设计师们很快就能够熟练的操作和使用,轻松审图和人性化的功能设计让设计师们的日常工作轻松了很多。单调沉闷的环境一去不复返,建筑设计院里开始充满着年轻活跃的氛围,不知不觉之间,先前在年轻人中间流传地那个段子慢慢地被人忘记了。 I SMART智能审图及远程互动审图一体机(以下简称一体机)为何如此"神奇"?它到底怎样解决图纸审看和会议沟通的障碍问题?谈到这个问题时,秦工饶有兴致地说起了一体机的应用实例,脸上洋溢着满意的神情。

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15、wakeup醒来/唤醒, 16、looklike 看起来像, 17、falldown倒下/落下 二、重要句子(语法) 、will+动词原形 将要做 2、fewer/more+可数名词复数更少/更多… 3、less/more+不可数名词 更少/更多 4、trytodosth. 尽力做某事 5、havetodosth 不得不做某事 6、agreewithsb. 同意某人的意见 7、such+名词(词组) 如此

8、playapartindoingsth 参与做某事 9、makesbdosth 让某人做某事 10、helpsbwithsth 帮助某人做某事 11、Therewillbe+主语+其他 将会有…. 12、Thereis/are+sb.+doingsth 有…正在做… 13、Itis +形容词+forsb+todosth 做某事对某人来说… 语法: whatwillthefuturebelike? citieswillbemorepolluted.Andtherewillbefewertrees. willpeopleusemoneyin100years?

【K12学习】人教版八年级英语上册Unit7知识点归纳整理

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aesb.dosth.helpsb.ithsth.帮助某人做某事 trytodosth.尽力做某事 It’s+ad+forsb.todosth.对某人来说,做某事……的。 语法讲解 Boosillonlybeonputers,notonpaper.书将只在电脑里,而不是在纸上。 Thereillbeorepollution.将会有更多的污染。 ).Thereillbe+n=Thereis/aregoingtobe+n将会有…ThereisgoingtobeafootballatchthisFriday. ).pollution:污染;公害pollute:污染;弄脏polluted:受污染的 Everyoneshouldplayapartinsavingtheearth.每个人应该参与挽救地球。 Todaytherearealreadyrobotsoringinfactories.现在已经有机器人在工厂里工作了。 Therebesb.doingsth.有某人正在做…Thereisabirdsinginginthetree. Theyagreeitaytaehundredsofyears.他们同意这可能花费几百年的时间。 Ittaes+时间+todosth.某人花费时间区做某事。 Ittooehalfanhourtofinishyhoeor. agreetodosth.eagreetoeetuplaterandtalthingsover

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curriculuords:litter,botto,fisheran Usefulexpressions:befullof,put…into,thro…into,cleanup,playapartin,closedon 过程 方法Accordingtodesigningsoetass,totrainstudents’listeningabilityandtotrainstudents’unicativepetence. 情感 态度Everyoneshouldeepourriversclean. 学习策略Listeningforeyords,transforinginforation. 重点TargetLanguage 难点1.Hototrainstudents’listeningability. .Hototrainstudents’unicativepetence. 教学内容及问题情境学生活动设计意图 a.TointroduceSstotheunitgoal,talaboutpollutionanden vironentalprotection. Picture: Thefourpicturesshodifferentforsofenvironentalpollut

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(会议管理)综合会议管理系统介绍

...../ ?更多资料请访问.(.....) ...../ 1.1 MCU设备技术要求RMX512C 1)基本要求 ?MCU采用一体化化的体系结构,满足电信级7×24稳定性、可靠性及冗余电源能力; ?支持H.323标准,支持SIP协议。 ?MCU必须支持推荐的标清终端及高清终端的混合进入同组会议,且可以保证高清及标清在会议中混合而不会降低系统容量。 ?MCU完全支持H.323,单台MCU必须可以支持12个4M视频端口系统容量。 ?可支持软件终端加入会议,并支持软件终端通过软件发送电脑图像到各会场。 ?具有较高的可靠性,MTBF为10万小时。 2)视频要求 ?视频编码支持ITU的H.26l、H.263、H.263+、H.264。 ?支持ITU-T标准的H.239双流标准。 ?实况视频分辨率可以支持QCIF、CIF、2CIF、4CIF、720P、1080P。 ?双流分辨率以支持VGA、SVGA、XGA、720P。 ?支持任意音频协议、视频协议、图像格式、速率接入同一个会议; ?支持N+1分屏模式,支持16分屏,满足24种分屏的布局模式; ?发送双流情况下,主视频流依然保持1280×720p的分辨率。

3)音频要求 ?音频编码支持G.711、G.722,、G.723.1C、G.728、G.719(siren 22)等。 ?用户及管理员静音控制 ?设备应该支持纯语音终端接入会议的能力。 4)管理要求 ?MCU的管理系统应采用专用的管理软件(非WEB方式),并实现全中文的管理方式。 ?支持语音激励模式、演讲者模式、导演模式、轮询会议。 ?支持远端镜头遥控FECC功能。 ?MCU应支持会议通过密码设置保证会议的安全和独立。 ?支持能够显示中文会场名及中文滚动字幕。 ?支持终端发起会议 ?支持会议主席在会场进行会议的控制,比如静音其它会场,以及结束会议等管理手段; 5)安全要求 ?AES加密 ?分级许可管理 6) 容量与速率 ?MCU容量不受分屏、速率、混协议的影响, ?H.323最大速率可达4M; ?支持高标清在同一会议,以满足将来系统扩展需要。 ?支持视频与音频1:1备份。 7)其它要求 ?可安装于19寸标准机柜。 ?MCU支持网络纠错补偿技术,保障在5%丢包情况下会议的效果。 ?MCU具备2个以上网络接口,支持跨网段会议。 ?MCU支持DiffServe、IP优先级,动态抖动缓冲器,语音及视频差错消隐技术的

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