Transformation of Free-text Electronic Health Records for Efficient Information Retrieval and Support of Knowledge Discovery

Size: px
Start display at page:

Download "Transformation of Free-text Electronic Health Records for Efficient Information Retrieval and Support of Knowledge Discovery"

Transcription

1 Transformation of Free-text Electronic Health Records for Efficient Information Retrieval and Support of Knowledge Discovery Jan Paralic, Peter Smatana Technical University of Kosice, Slovakia Center for Information Technologies Abstract: One of the major problems in e-health domain is electronic processing of patient health records. The core of the problem is transforming original, free-text health records to structured documents by using a defined structure model. We designed a system, which use combination of two approaches to do this transformation. Regular was used to recognition typical patterns like date or various biophysical parameters. Linguistic was used to analyze sentences and blocks in a text. Resulted structured documents can be used not only for efficient concept-based information retrieval, but, e.g. also for knowledge discovery process in collections of structured electronic patient records. Keywords: patient records, e-health, regular, linguistic, structured patient records 1. ITRODUCTIO Original patient health records are often written by physicians in the form of unstructured text that is not suitable for efficient electronic processing [7]. On the other hand, their electronic version could significantly enhance the possibilities for information retrieval, as well as further of patient records for various purposes [5]. Many publications have suggested positive effects of electronic messaging in health care [9]. From the electronic patient records could profit all groups of stakeholders in the e-health domain providing the possibility to create various types of useful and efficient electronic services [2]. Much more efficient information retrieval by all types of stakeholders (patients about her/his health status, physicians, health insurance companies) can be used e.g. for the following purposes. Sharing of all available information about patients between various departments in a hospital, as well as between various types of medical specialists, avoiding e.g. unnecessary repetition of similar investigations. Generation of pre-filled forms of various types by patients, physicians as well as by insurance companies). Efficient decision support is possible when a large number of electronic health records from history are available and can be used e.g. as follows. Decision support for various physicians, who could consult similar cases from the history. Discovery of new, interesting patterns, which can result in new knowledge about particular decease, effects of pharmaceuticals etc. (for more details see Section 4). Efficient of particular physicians behavior by health insurance companies. The rest of this paper is structured as follows. Section 2 presents the proposed system. First, some basic characteristics of the real data and their common features are summarized. Second, the functional architecture of the proposed and implemented system is provided and explained. Section 3 describes experiments performed with the implemented system on real data from two hospitals. In section 4 some future possibilities to exploit available structured data using knowledge discovery techniques are sketched. Finally section 5 summarizes main contributions of this paper.

2 2. SYSTEM DESIG 2.1. DATA AALYSIS The core problem in the area of electronic health records is transformation of original, free-text health records to structured documents by using a defined structure model. Therefore detailed data was necessary in order to design a suitable architecture for our system. We have available real data from The European Center for Medical Informatics, Statistics and Epidemiology of Charles University and Academy of Sciences (EuroMISE Center 1 ). Data are representing by anonymous health records, which are structured to blocks (social anamnesis, family history, ECG, etc.). In individual blocks are data written as free-text. The data was acquired from cardio clinics in two different hospitals in Czech republic. Basic statistical characteristics of the data are provided in Table 1. regular morphological syntactic training data (hospital 1) testing data (hospital 1) testing data (hospital 2) o. of records Text size [B] found patterns patterns/record 8,90 11,81 10,90 patterns/kb of text 6,49 6,61 5,18 found patterns patterns/record 7,85 10,60 17,37 patterns/kb of text 5,72 5,93 8,25 found patterns patterns/record 0,30 0,35 0,93 patterns/kb of text 0,22 0,20 0,44 Table 1: Statistical characteristics of the given data sets There are some common problems that we identified having analyzed available health records data, e.g.: All physicians who produced the health records have their own, unique writing style. Moreover, there are some small, but notable differences in terminology used in different hospitals, which is implied by different work habits. Significant number of typist s errors. Heavy usage of different acronyms (which may differ also for particular hospitals). Data in individual blocks are mixed. We have also available a structured model (in form of a taxonomy) from EuroMISE Center, which we need to fill in with information extracted patient from health records text. The model consists e.g. from features like blood pressure, pulse, characteristic of ECG, number of smoked cigarettes per day, allergy, etc. 1

3 2.2. SYSTEM ARCHITECTURE Figure 1. shows functional scheme of the proposed system, which uses a combination of regular and linguistic. Use of linguistic approaches is especially difficult for languages like Czech or Slovak [1], [4]. The system is being implemented using Java technology. Already implemented blocks are marked with bold border in Figure 1. 1.Free-text document 2.Regular 3.Tokenization 4.Morphological 5.Identify blocks 6.Syntactic 7.Semantic 8.Context 9.Mapping to data model 10.Structured document Figure 1: Functional architecture of the proposed system Functionality of particular building blocks in the proposed sequential architecture is briefly explained in the following: 1. Free-text document: Given free-text patient health record. 2. Regular : Looking for regular expressions (example: blood pressure TK120/30 we describe in the form of following regular expression: TK\d+/\d+), which are used as special words in next step of. This was the only type of used in system proposed in [8]. 3. Tokenization: Division document to individual tokens (i.e. words or regular expressions identified in previous step). 4. Morphological : Specifying word class to individual words (such as nouns, verbs, etc.) and their grammatical categories [4], [1]. 5. Identify blocks: It would be useful to identify particular text blocks such as e.g. family history block. But this is not easy, because sentences are not exactly defined, physicians often do not use regular sentences. 6. Syntactic : We are looking for simple sentences (if not a whole sentence, then at least some verb phrases), because they have got big information value. Mainly we are searching for subjects and predicates [4]. 7. Semantic : As next step would be to define relations between words, which for Slovak or Czech language would imply the necessity to have special dictionary with semantic bindings of particular verbs [4], [1]. 8. Context : In case that a sentence doe not have a direct object, it is necessary to derive it from a context [4]. But this is not so typical problem for patient health records. 9. Mapping to data model: This is block is to recognize patterns using given data model and results of the previous regular and linguistic and saving recognized patterns to data model. 10. Structured document: Structured document in XML format presents the output of our system.

4 3. EXPERIMETS We used the same data to train and to test as in [8], where regular only has been used to structure patient health records. Our goal was to increase the resulted precision recognized data model structures in free-text. To evaluate of quality of transformation we used coefficients P (precision), R (recall) and F -measure, defined by equations 1, 2 and 3 respectively. To evaluate F we used = 0,5 (harmonic mean of P and R). Precision: marked_relevant P = 0; 1. (1) marked Recall: marked_relevant R = 0; 1. (2) relevant F -measure: = PR F 0; ( 1 β ) P + βr 1 β. (3) Where: marked_relevant - number of all correctly recognized (marked) expression by the system as relevant to the given data model - number of all expression recognized (marked) by the system as relevant to the given marked data model - number of all expressions in the text, that are relevant to the given data model relevant First, the system was trained with data from patient health records from hospital 1 only. ext, we evaluated the influence of adding linguistic (blocks 3, 4 and 6 in Figure 1) to the regular one (block 2 in Figure 1). The detailed results of experiments are presented in Table 2 (regular only) and in Table 3 (regular as well as linguistic ). file marked fault unmarked P R F txt ,80 0,80 0,80 39.txt ,81 0,62 0,70 61.txt ,00 0,75 0,86 75.txt ,00 0,80 0, txt ,82 0,90 0,86 20.txt ,75 0,75 0,75 65.txt ,60 0,30 0, txt ,93 0,81 0,87 64.txt ,00 0,75 0,86 98.txt ,00 0,82 0,90 total ,88 0,72 0,79 Table 2: Patterns found in data from hospital 1 using regular only

5 file marked fault unmarked P R F txt ,80 0,80 0,80 39.txt ,81 0,62 0,70 61.txt ,00 0,75 0,86 75.txt ,00 0,80 0, txt ,82 0,90 0,86 20.txt ,88 0,88 0,88 65.txt ,80 0,40 0, txt ,93 0,81 0,87 64.txt ,00 0,75 0,86 98.txt ,00 0,82 0,90 total ,90 0,74 0,81 Table 3: Patterns found in data from hospital 1 using regular and linguistic From the tables above we can see, that in two cases from the 10 analyzed documents the results have been improved, namely in patient records 20.txt and 65.txt (they are marked with bold font in both Table 2 and Table 3). In the last experiment we applied our system, trained on data from the hospital 1 only to test data from hospital 2. Detailed results of this experiment are presented in Table 4. We can see that the results are a bit worse in comparison to the previous experiment (see Table 3), but in an acceptable measure. The reason is that physicians in the second hospital have a bit different terminology, use different abbreviations etc. that could not be acquired during the training process from patient health records from other hospital. file marked fault unmarked P R F txt ,82 0,60 0,69 06.txt ,00 0,69 0,81 09.txt ,25 0,22 0,24 13.txt ,86 1,00 0,92 20.txt ,40 0,20 0,27 28.txt ,69 0,75 0,72 24.txt ,91 0,77 0,83 27.txt ,82 1,00 0,90 11.txt ,92 0,92 0,92 17.txt ,73 0,55 0,63 total ,76 0,66 0,71 Table 4: Patterns found in data from hospital 2 using regular and linguistic 4. POSSIBLE USE OF TEXT MIIG There are many possibilities for application of text (data) mining approaches for discovery of new and potentially useful patterns from large number of electronic patients health records. In this section some of these possibilities are sketched. Classification/prediction models - can be built on patient medical records data with known value of the target attribute (e.g. diagnosis). For new patients this classification models can suggest the value of the target attribute [3]. Another application of this predictive text mining approaches is to annotate patient records [5] and/or populate existing ontology with instances [10]. Clustering and descriptive data mining clustering and suitable visualization of discovered clusters of patients [5]. Moreover, descriptive data mining techniques may be employed in order to digestedly describe the main characteristics of patients from one cluster.

6 Dialog system produced classification/prediction models, clusters with their descriptions as well as discovered association rules (see e.g. [6]) may be used within a dialog system. This could be a support tool produced e.g. in form of an electronic service, which could help e.g. doctors when facing a new patient (ask for e.g. characterization of similar patients from the same cluster, predicted diagnosis, or known associations for given data about patient etc.). 5. COCLUSIOS In this paper we presented the functional architecture and results achieved in first experiments with a system designed and implemented for transformation of free-text patient health records into a structured, XML format. By using a combination of regular and linguistic we improved the quality of free-text documents transformation when using training and testing set of documents from the same hospital. When documents for training and testing are from different hospitals, improvement of the efficiency measures could not be observed. Presented system is not perfect, but transformation of electronic patient health records into a structured form is a big challenge and this system definitely means a first important step towards this goal. ACKOWLEDGEMETS The work presented in this paper was supported by the Slovak Grant Agency of Ministry of Education and Academy of Science of the Slovak Republic within the project Document classification and annotation for the Semantic web o. 1/1060/04 and by the German-Slovak research project DAAD o. 8/2004 Text Mining for Metadata Extraction and Semantic Retrieval. REFERECES [1] Furdik, K. (2003): Information retrieval in natural language making use of hypertext structures. Technical University of Kosice. PhD-thesis (in Slovak) [2] Hanzlicek, P. (2002): Development of Universal Electronic Health Record in Cardiology. Health Data in the Information Society: Surjan G., Engelbrecht R., Mcair P. (eds.) Amsterdam, IOS Press, pp [3] Machova, K. (2002): Machine Learning Principles and Algorithms. Elfa Press (in Slovak) [4] Pales, E. (1994): SAPFO Design of a Paraphrasing System for the Slovak Language. Bratislava, VEDA (in Slovak) [5] Paralic, J.; Bednar, P. (2004): Text Mining for Document Annotation and Ontology Support. Intelligent Systems at the Service of Mankind, Ubooks, Germany, pp [6] Rauch, J. (2001): Mining for Statistical Association Rules. In: Fong J., g M.K. (eds.): The 5 th Pacific-Asia Conference on Knowledge Discovery and Data Mining, University of Hong-Kong, pp [7] Schultz, S.; Hahn, U. (2001): Medical Knowledge Reengineering Converting Major Portions of the UMLS into Terminological Knowledge Base. Int. Journal on Medical Informatics, 64 (2-3), pp [8] Semecky, J. (2001): Multimedia electronic patient record in cardiology. Charles University in Prague. Diploma thesis (in Czech) [9] Van der Kam, W.; Moorman, P.W.; Koppejan-Mulder, M.J. (2000): Effects of Electronic Communication in General Practice. Int. Journal on Medical Informatics, 60 (1), pp [10] Maria Vargas-Vera, David Celjuska: Event Recognition on ews Stories and Semi-Automatic Population of an Ontology. IEEE/WIC/ACM Int. Conference on Web Intelligence (WI 2004), Beijing, China, pp

Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words

Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words , pp.290-295 http://dx.doi.org/10.14257/astl.2015.111.55 Efficient Techniques for Improved Data Classification and POS Tagging by Monitoring Extraction, Pruning and Updating of Unknown Foreign Words Irfan

More information

IT services for analyses of various data samples

IT services for analyses of various data samples IT services for analyses of various data samples Ján Paralič, František Babič, Martin Sarnovský, Peter Butka, Cecília Havrilová, Miroslava Muchová, Michal Puheim, Martin Mikula, Gabriel Tutoky Technical

More information

Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization

Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization Knowledge Discovery using Text Mining: A Programmable Implementation on Information Extraction and Categorization Atika Mustafa, Ali Akbar, and Ahmer Sultan National University of Computer and Emerging

More information

Natural Language to Relational Query by Using Parsing Compiler

Natural Language to Relational Query by Using Parsing Compiler Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 3, March 2015,

More information

How the Computer Translates. Svetlana Sokolova President and CEO of PROMT, PhD.

How the Computer Translates. Svetlana Sokolova President and CEO of PROMT, PhD. Svetlana Sokolova President and CEO of PROMT, PhD. How the Computer Translates Machine translation is a special field of computer application where almost everyone believes that he/she is a specialist.

More information

Content-based Retrieval of Analytic Reports

Content-based Retrieval of Analytic Reports Content-based Retrieval of Analytic Reports Václav Lín 1, Jan Rauch 1,2, and Vojtěch Svátek 1,2 1 Department of Information and Knowledge Engineering, University of Economics, Prague, W. Churchill Sq.

More information

Survey Results: Requirements and Use Cases for Linguistic Linked Data

Survey Results: Requirements and Use Cases for Linguistic Linked Data Survey Results: Requirements and Use Cases for Linguistic Linked Data 1 Introduction This survey was conducted by the FP7 Project LIDER (http://www.lider-project.eu/) as input into the W3C Community Group

More information

Research on News Video Multi-topic Extraction and Summarization

Research on News Video Multi-topic Extraction and Summarization International Journal of New Technology and Research (IJNTR) ISSN:2454-4116, Volume-2, Issue-3, March 2016 Pages 37-39 Research on News Video Multi-topic Extraction and Summarization Di Li, Hua Huo Abstract

More information

Information Systems & Semantic Web University of Koblenz Landau, Germany

<is web> Information Systems & Semantic Web University of Koblenz Landau, Germany Information Systems University of Koblenz Landau, Germany Semantic Multimedia Management - Multimedia Annotation Tools http://isweb.uni-koblenz.de Multimedia Annotation Different levels of annotations

More information

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10

131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10 1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom

More information

Recognition and Privacy Preservation of Paper-based Health Records

Recognition and Privacy Preservation of Paper-based Health Records Quality of Life through Quality of Information J. Mantas et al. (Eds.) IOS Press, 2012 2012 European Federation for Medical Informatics and IOS Press. All rights reserved. doi:10.3233/978-1-61499-101-4-751

More information

Internet of Things, data management for healthcare applications. Ontology and automatic classifications

Internet of Things, data management for healthcare applications. Ontology and automatic classifications Internet of Things, data management for healthcare applications. Ontology and automatic classifications Inge.Krogstad@nor.sas.com SAS Institute Norway Different challenges same opportunities! Data capture

More information

Model Driven Interoperability through Semantic Annotations using SoaML and ODM

Model Driven Interoperability through Semantic Annotations using SoaML and ODM Model Driven Interoperability through Semantic Annotations using SoaML and ODM JiuCheng Xu*, ZhaoYang Bai*, Arne J.Berre*, Odd Christer Brovig** *SINTEF, Pb. 124 Blindern, NO-0314 Oslo, Norway (e-mail:

More information

Master Specialization in Knowledge Engineering

Master Specialization in Knowledge Engineering Master Specialization in Knowledge Engineering Pavel Kordík, Ph.D. Department of Computer Science Faculty of Information Technology Czech Technical University in Prague Prague, Czech Republic http://www.fit.cvut.cz/en

More information

Semantic annotation of requirements for automatic UML class diagram generation

Semantic annotation of requirements for automatic UML class diagram generation www.ijcsi.org 259 Semantic annotation of requirements for automatic UML class diagram generation Soumaya Amdouni 1, Wahiba Ben Abdessalem Karaa 2 and Sondes Bouabid 3 1 University of tunis High Institute

More information

Distributed Knowledge Management based on Software Agents and Ontology

Distributed Knowledge Management based on Software Agents and Ontology Distributed Knowledge Management based on Software Agents and Ontology Michal Laclavik 1, Zoltan Balogh 1, Ladislav Hluchy 1, Renata Slota 2, Krzysztof Krawczyk 3 and Mariusz Dziewierz 3 1 Institute of

More information

Actionable Awareness. 5/12/2015 TEI Proprietary TEI Proprietary

Actionable Awareness. 5/12/2015 TEI Proprietary TEI Proprietary Actionable Awareness Data - well defined, pedigreed, and connected. Information intelligently integrated data Knowledge carefully applied information to a subject area Actionable Awareness correctly applied

More information

ACQUIRING, ORGANISING AND PRESENTING INFORMATION AND KNOWLEDGE ON THE WEB. Pavol Návrat

ACQUIRING, ORGANISING AND PRESENTING INFORMATION AND KNOWLEDGE ON THE WEB. Pavol Návrat Computing and Informatics, Vol. 28, 2009, 393 398 ACQUIRING, ORGANISING AND PRESENTING INFORMATION AND KNOWLEDGE ON THE WEB Pavol Návrat Institute of Informatics and Software Engineering Faculty of Informatics

More information

Find the signal in the noise

Find the signal in the noise Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical

More information

Folksonomies versus Automatic Keyword Extraction: An Empirical Study

Folksonomies versus Automatic Keyword Extraction: An Empirical Study Folksonomies versus Automatic Keyword Extraction: An Empirical Study Hend S. Al-Khalifa and Hugh C. Davis Learning Technology Research Group, ECS, University of Southampton, Southampton, SO17 1BJ, UK {hsak04r/hcd}@ecs.soton.ac.uk

More information

EXPLOITING FOLKSONOMIES AND ONTOLOGIES IN AN E-BUSINESS APPLICATION

EXPLOITING FOLKSONOMIES AND ONTOLOGIES IN AN E-BUSINESS APPLICATION EXPLOITING FOLKSONOMIES AND ONTOLOGIES IN AN E-BUSINESS APPLICATION Anna Goy and Diego Magro Dipartimento di Informatica, Università di Torino C. Svizzera, 185, I-10149 Italy ABSTRACT This paper proposes

More information

Web Mining. Margherita Berardi LACAM. Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it

Web Mining. Margherita Berardi LACAM. Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it Web Mining Margherita Berardi LACAM Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it Bari, 24 Aprile 2003 Overview Introduction Knowledge discovery from text (Web Content

More information

Flattening Enterprise Knowledge

Flattening Enterprise Knowledge Flattening Enterprise Knowledge Do you Control Your Content or Does Your Content Control You? 1 Executive Summary: Enterprise Content Management (ECM) is a common buzz term and every IT manager knows it

More information

Search and Information Retrieval

Search and Information Retrieval Search and Information Retrieval Search on the Web 1 is a daily activity for many people throughout the world Search and communication are most popular uses of the computer Applications involving search

More information

An Ontology Based Method to Solve Query Identifier Heterogeneity in Post- Genomic Clinical Trials

An Ontology Based Method to Solve Query Identifier Heterogeneity in Post- Genomic Clinical Trials ehealth Beyond the Horizon Get IT There S.K. Andersen et al. (Eds.) IOS Press, 2008 2008 Organizing Committee of MIE 2008. All rights reserved. 3 An Ontology Based Method to Solve Query Identifier Heterogeneity

More information

Enabling Business Experts to Discover Web Services for Business Process Automation. Emerging Web Service Technologies

Enabling Business Experts to Discover Web Services for Business Process Automation. Emerging Web Service Technologies Enabling Business Experts to Discover Web Services for Business Process Automation Emerging Web Service Technologies Jan-Felix Schwarz 3 December 2009 Agenda 2 Problem & Background Approach Evaluation

More information

A Statistical Text Mining Method for Patent Analysis

A Statistical Text Mining Method for Patent Analysis A Statistical Text Mining Method for Patent Analysis Department of Statistics Cheongju University, shjun@cju.ac.kr Abstract Most text data from diverse document databases are unsuitable for analytical

More information

Key Technology Study of Agriculture Information Cloud-Services

Key Technology Study of Agriculture Information Cloud-Services Key Technology Study of Agriculture Information Cloud-Services Yunpeng Cui, Shihong Liu Key Laboratory of Digital Agricultural Early-warning Technology, Ministry of Agriculture, Beijing, The People s epublic

More information

UIMA: Unstructured Information Management Architecture for Data Mining Applications and developing an Annotator Component for Sentiment Analysis

UIMA: Unstructured Information Management Architecture for Data Mining Applications and developing an Annotator Component for Sentiment Analysis UIMA: Unstructured Information Management Architecture for Data Mining Applications and developing an Annotator Component for Sentiment Analysis Jan Hajič, jr. Charles University in Prague Faculty of Mathematics

More information

Research of Postal Data mining system based on big data

Research of Postal Data mining system based on big data 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA 2015) Research of Postal Data mining system based on big data Xia Hu 1, Yanfeng Jin 1, Fan Wang 1 1 Shi Jiazhuang Post & Telecommunication

More information

Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System

Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System Architecture of an Ontology-Based Domain- Specific Natural Language Question Answering System Athira P. M., Sreeja M. and P. C. Reghuraj Department of Computer Science and Engineering, Government Engineering

More information

Mining Signatures in Healthcare Data Based on Event Sequences and its Applications

Mining Signatures in Healthcare Data Based on Event Sequences and its Applications Mining Signatures in Healthcare Data Based on Event Sequences and its Applications Siddhanth Gokarapu 1, J. Laxmi Narayana 2 1 Student, Computer Science & Engineering-Department, JNTU Hyderabad India 1

More information

Skills for Effective Business Communication: Efficiency, Collaboration, and Success

Skills for Effective Business Communication: Efficiency, Collaboration, and Success Skills for Effective Business Communication: Efficiency, Collaboration, and Success Michael Shorenstein Center for Communication Kennedy School of Government Harvard University September 30, 2014 I: Introduction

More information

Modeling Temporal Data in Electronic Health Record Systems

Modeling Temporal Data in Electronic Health Record Systems International Journal of Information Science and Intelligent System, 3(3): 51-60, 2014 Modeling Temporal Data in Electronic Health Record Systems Chafiqa Radjai 1, Idir Rassoul², Vytautas Čyras 3 1,2 Mouloud

More information

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 5 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 5 ISSN 2229-5518 International Journal of Scientific & Engineering Research, Volume 4, Issue 11, November-2013 5 INTELLIGENT MULTIDIMENSIONAL DATABASE INTERFACE Mona Gharib Mohamed Reda Zahraa E. Mohamed Faculty of Science,

More information

Digital archiving of scientific information Czech experience

Digital archiving of scientific information Czech experience Digital archiving of scientific information Czech experience P. Slavik, P. Mach, M. Snorek Czech Technical University in Prague Prague, Czech Republic Slavik mach snorek@fel.cvut.cz Abstract This paper

More information

Electronic Health Records in Continuous Shared Dental Care

Electronic Health Records in Continuous Shared Dental Care Electronic Health Records in Continuous Shared Dental Care Taťjana Dostálová 1, Jana Zvárová 2, Zuzana Teuberová 1, Michaela Seydlová 1, Martin Pieš 2, Josef Špidlen 2 1 Department of Prosthodontics, First

More information

Inference of ICD Codes by Rule-Based Method from. Medical Record in NTCIR-12 MedNLPDoc

Inference of ICD Codes by Rule-Based Method from. Medical Record in NTCIR-12 MedNLPDoc Inference of ICD Codes by Rule-Based Method from Medical Record in NTCIR-12 MedNLPDoc Masahito Sakishita Faculty of Informatics, Shizuoka University msakishita@kanolab.net Yoshinobu Kano Faculty of Informatics,

More information

The Development of Multimedia-Multilingual Document Storage, Retrieval and Delivery System for E-Organization (STREDEO PROJECT)

The Development of Multimedia-Multilingual Document Storage, Retrieval and Delivery System for E-Organization (STREDEO PROJECT) The Development of Multimedia-Multilingual Storage, Retrieval and Delivery for E-Organization (STREDEO PROJECT) Asanee Kawtrakul, Kajornsak Julavittayanukool, Mukda Suktarachan, Patcharee Varasrai, Nathavit

More information

SemWeB Semantic Web Browser Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks

SemWeB Semantic Web Browser Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks SemWeB Semantic Web Browser Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks Melike Şah, Wendy Hall and David C De Roure Intelligence, Agents and Multimedia Group,

More information

IBM Watson s Next Step: Health. All About the Data January 21 st 2016, Groningen

IBM Watson s Next Step: Health. All About the Data January 21 st 2016, Groningen IBM Watson s Next Step: Health All About the Data January 21 st 2016, Groningen Introduction speaker Dr Nicky S. Hekster Technical Leader Healthcare & LifeSciences IBM Nederland BV Johan Huizingalaan 765

More information

SOFTWARE ENGINEERING PROGRAM

SOFTWARE ENGINEERING PROGRAM SOFTWARE ENGINEERING PROGRAM PROGRAM TITLE DEGREE TITLE Master of Science Program in Software Engineering Master of Science (Software Engineering) M.Sc. (Software Engineering) PROGRAM STRUCTURE Total program

More information

ENABLING SEMANTIC SEARCH IN STRUCTURED P2P NETWORKS VIA DISTRIBUTED DATABASES AND WEB SERVICES

ENABLING SEMANTIC SEARCH IN STRUCTURED P2P NETWORKS VIA DISTRIBUTED DATABASES AND WEB SERVICES ENABLING SEMANTIC SEARCH IN STRUCTURED P2P NETWORKS VIA DISTRIBUTED DATABASES AND WEB SERVICES Maria Teresa Andrade FEUP / INESC Porto mandrade@fe.up.pt ; maria.andrade@inescporto.pt http://www.fe.up.pt/~mandrade/

More information

Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg

Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg Module Catalogue for the Bachelor Program in Computational Linguistics at the University of Heidelberg March 1, 2007 The catalogue is organized into sections of (1) obligatory modules ( Basismodule ) that

More information

Big Data and Text Mining

Big Data and Text Mining Big Data and Text Mining Dr. Ian Lewin Senior NLP Resource Specialist Ian.lewin@linguamatics.com www.linguamatics.com About Linguamatics Boston, USA Cambridge, UK Software Consulting Hosted content Agile,

More information

Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis

Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Yue Dai, Ernest Arendarenko, Tuomo Kakkonen, Ding Liao School of Computing University of Eastern Finland {yvedai,

More information

Why SBVR? Donald Chapin. Chair, OMG SBVR Revision Task Force Business Semantics Ltd Donald.Chapin@BusinessSemantics.com

Why SBVR? Donald Chapin. Chair, OMG SBVR Revision Task Force Business Semantics Ltd Donald.Chapin@BusinessSemantics.com Why SBVR? Towards a Business Natural Language (BNL) for Financial Services Panel Demystifying Financial Services Semantics Conference New York,13 March 2012 Donald Chapin Chair, OMG SBVR Revision Task

More information

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM. DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,

More information

Parsing Software Requirements with an Ontology-based Semantic Role Labeler

Parsing Software Requirements with an Ontology-based Semantic Role Labeler Parsing Software Requirements with an Ontology-based Semantic Role Labeler Michael Roth University of Edinburgh mroth@inf.ed.ac.uk Ewan Klein University of Edinburgh ewan@inf.ed.ac.uk Abstract Software

More information

DISTRIBUTED ARCHITECTURE FOR ELECTRONIC HEALTH REFERRAL SYSTEM UTILIZING COMPUTATIONAL INTELLIGENCE FOR CLINICAL DECISION SUPPORT

DISTRIBUTED ARCHITECTURE FOR ELECTRONIC HEALTH REFERRAL SYSTEM UTILIZING COMPUTATIONAL INTELLIGENCE FOR CLINICAL DECISION SUPPORT DISTRIBUTED ARCHITECTURE FOR ELECTRONIC HEALTH REFERRAL SYSTEM UTILIZING COMPUTATIONAL INTELLIGENCE FOR CLINICAL DECISION SUPPORT By Majd Misbah Al-Zghoul Supervisor Dr. Majid Al-Taee, Prof. This Thesis

More information

Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features

Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features , pp.273-280 http://dx.doi.org/10.14257/ijdta.2015.8.4.27 Tibetan-Chinese Bilingual Sentences Alignment Method based on Multiple Features Lirong Qiu School of Information Engineering, MinzuUniversity of

More information

REVIEW ON QUERY CLUSTERING ALGORITHMS FOR SEARCH ENGINE OPTIMIZATION

REVIEW ON QUERY CLUSTERING ALGORITHMS FOR SEARCH ENGINE OPTIMIZATION Volume 2, Issue 2, February 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: A REVIEW ON QUERY CLUSTERING

More information

Blog Post Extraction Using Title Finding

Blog Post Extraction Using Title Finding Blog Post Extraction Using Title Finding Linhai Song 1, 2, Xueqi Cheng 1, Yan Guo 1, Bo Wu 1, 2, Yu Wang 1, 2 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 2 Graduate School

More information

Interactive Dynamic Information Extraction

Interactive Dynamic Information Extraction Interactive Dynamic Information Extraction Kathrin Eichler, Holmer Hemsen, Markus Löckelt, Günter Neumann, and Norbert Reithinger Deutsches Forschungszentrum für Künstliche Intelligenz - DFKI, 66123 Saarbrücken

More information

Clinical and research data integration: the i2b2 FSM experience

Clinical and research data integration: the i2b2 FSM experience Clinical and research data integration: the i2b2 FSM experience Laboratory of Biomedical Informatics for Clinical Research Fondazione Salvatore Maugeri - FSM - Hospital, Pavia, italy Laboratory of Biomedical

More information

Introduction. A. Bellaachia Page: 1

Introduction. A. Bellaachia Page: 1 Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.

More information

ONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS

ONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS ONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS Divyanshu Chandola 1, Aditya Garg 2, Ankit Maurya 3, Amit Kushwaha 4 1 Student, Department of Information Technology, ABES Engineering College, Uttar Pradesh,

More information

Extraction of Legal Definitions from a Japanese Statutory Corpus Toward Construction of a Legal Term Ontology

Extraction of Legal Definitions from a Japanese Statutory Corpus Toward Construction of a Legal Term Ontology Extraction of Legal Definitions from a Japanese Statutory Corpus Toward Construction of a Legal Term Ontology Makoto Nakamura, Yasuhiro Ogawa, Katsuhiko Toyama Japan Legal Information Institute, Graduate

More information

Ontology construction on a cloud computing platform

Ontology construction on a cloud computing platform Ontology construction on a cloud computing platform Exposé for a Bachelor's thesis in Computer science - Knowledge management in bioinformatics Tobias Heintz 1 Motivation 1.1 Introduction PhenomicDB is

More information

Software Architecture Document

Software Architecture Document Software Architecture Document Natural Language Processing Cell Version 1.0 Natural Language Processing Cell Software Architecture Document Version 1.0 1 1. Table of Contents 1. Table of Contents... 2

More information

What you can accomplish with IBMContent Analytics

What you can accomplish with IBMContent Analytics What you can accomplish with IBMContent Analytics An Enterprise Content Management solution What is IBM Content Analytics? Alex On February 14-16, IBM s Watson computing system made its television debut

More information

Big Data Text Mining and Visualization. Anton Heijs

Big Data Text Mining and Visualization. Anton Heijs Copyright 2007 by Treparel Information Solutions BV. This report nor any part of it may be copied, circulated, quoted without prior written approval from Treparel7 Treparel Information Solutions BV Delftechpark

More information

ONTOLOGY BASED FEEDBACK GENERATION IN DESIGN- ORIENTED E-LEARNING SYSTEMS

ONTOLOGY BASED FEEDBACK GENERATION IN DESIGN- ORIENTED E-LEARNING SYSTEMS ONTOLOGY BASED FEEDBACK GENERATION IN DESIGN- ORIENTED E-LEARNING SYSTEMS Harrie Passier and Johan Jeuring Faculty of Computer Science, Open University of the Netherlands Valkenburgerweg 177, 6419 AT Heerlen,

More information

USING SPATIAL DATA MINING TO DISCOVER THE HIDDEN RULES IN THE CRIME DATA

USING SPATIAL DATA MINING TO DISCOVER THE HIDDEN RULES IN THE CRIME DATA USING SPATIAL DATA MINING TO DISCOVER THE HIDDEN RULES IN THE CRIME DATA Karel, JANEČKA 1, Hana, HŮLOVÁ 1 1 Department of Mathematics, Faculty of Applied Sciences, University of West Bohemia Abstract Univerzitni

More information

SEMANTIC VIDEO ANNOTATION IN E-LEARNING FRAMEWORK

SEMANTIC VIDEO ANNOTATION IN E-LEARNING FRAMEWORK SEMANTIC VIDEO ANNOTATION IN E-LEARNING FRAMEWORK Antonella Carbonaro, Rodolfo Ferrini Department of Computer Science University of Bologna Mura Anteo Zamboni 7, I-40127 Bologna, Italy Tel.: +39 0547 338830

More information

ISSN: 2348 9510. A Review: Image Retrieval Using Web Multimedia Mining

ISSN: 2348 9510. A Review: Image Retrieval Using Web Multimedia Mining A Review: Image Retrieval Using Web Multimedia Satish Bansal*, K K Yadav** *, **Assistant Professor Prestige Institute Of Management, Gwalior (MP), India Abstract Multimedia object include audio, video,

More information

CAPTURING THE VALUE OF UNSTRUCTURED DATA: INTRODUCTION TO TEXT MINING

CAPTURING THE VALUE OF UNSTRUCTURED DATA: INTRODUCTION TO TEXT MINING CAPTURING THE VALUE OF UNSTRUCTURED DATA: INTRODUCTION TO TEXT MINING Mary-Elizabeth ( M-E ) Eddlestone Principal Systems Engineer, Analytics SAS Customer Loyalty, SAS Institute, Inc. Is there valuable

More information

Using text mining to understand the call center customers claims

Using text mining to understand the call center customers claims Data Mining VII: Data, Text and Web Mining and their Business Applications 177 Using text mining to understand the call center customers claims G. M. Caputo, V. M. Bastos & N. F. F. Ebecken COPPE Federal

More information

Delivering Smart Answers!

Delivering Smart Answers! Companion for SharePoint Topic Analyst Companion for SharePoint All Your Information Enterprise-ready Enrich SharePoint, your central place for document and workflow management, not only with an improved

More information

A Survey on Web Mining From Web Server Log

A Survey on Web Mining From Web Server Log A Survey on Web Mining From Web Server Log Ripal Patel 1, Mr. Krunal Panchal 2, Mr. Dushyantsinh Rathod 3 1 M.E., 2,3 Assistant Professor, 1,2,3 computer Engineering Department, 1,2 L J Institute of Engineering

More information

Text Mining: The state of the art and the challenges

Text Mining: The state of the art and the challenges Text Mining: The state of the art and the challenges Ah-Hwee Tan Kent Ridge Digital Labs 21 Heng Mui Keng Terrace Singapore 119613 Email: ahhwee@krdl.org.sg Abstract Text mining, also known as text data

More information

Domain Classification of Technical Terms Using the Web

Domain Classification of Technical Terms Using the Web Systems and Computers in Japan, Vol. 38, No. 14, 2007 Translated from Denshi Joho Tsushin Gakkai Ronbunshi, Vol. J89-D, No. 11, November 2006, pp. 2470 2482 Domain Classification of Technical Terms Using

More information

Selected Topics in Applied Machine Learning: An integrating view on data analysis and learning algorithms

Selected Topics in Applied Machine Learning: An integrating view on data analysis and learning algorithms Selected Topics in Applied Machine Learning: An integrating view on data analysis and learning algorithms ESSLLI 2015 Barcelona, Spain http://ufal.mff.cuni.cz/esslli2015 Barbora Hladká hladka@ufal.mff.cuni.cz

More information

Activity Mining for Discovering Software Process Models

Activity Mining for Discovering Software Process Models Activity Mining for Discovering Software Process Models Ekkart Kindler, Vladimir Rubin, Wilhelm Schäfer Software Engineering Group, University of Paderborn, Germany [kindler, vroubine, wilhelm]@uni-paderborn.de

More information

Data Deduplication in Slovak Corpora

Data Deduplication in Slovak Corpora Ľ. Štúr Institute of Linguistics, Slovak Academy of Sciences, Bratislava, Slovakia Abstract. Our paper describes our experience in deduplication of a Slovak corpus. Two methods of deduplication a plain

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION Exploration is a process of discovery. In the database exploration process, an analyst executes a sequence of transformations over a collection of data structures to discover useful

More information

Efficient Query Optimizing System for Searching Using Data Mining Technique

Efficient Query Optimizing System for Searching Using Data Mining Technique Vol.1, Issue.2, pp-347-351 ISSN: 2249-6645 Efficient Query Optimizing System for Searching Using Data Mining Technique Velmurugan.N Vijayaraj.A Assistant Professor, Department of MCA, Associate Professor,

More information

Automatic Annotation Wrapper Generation and Mining Web Database Search Result

Automatic Annotation Wrapper Generation and Mining Web Database Search Result Automatic Annotation Wrapper Generation and Mining Web Database Search Result V.Yogam 1, K.Umamaheswari 2 1 PG student, ME Software Engineering, Anna University (BIT campus), Trichy, Tamil nadu, India

More information

A prototype infrastructure for D Spin Services based on a flexible multilayer architecture

A prototype infrastructure for D Spin Services based on a flexible multilayer architecture A prototype infrastructure for D Spin Services based on a flexible multilayer architecture Volker Boehlke 1,, 1 NLP Group, Department of Computer Science, University of Leipzig, Johanisgasse 26, 04103

More information

ONTOLOGY-BASED MULTIMEDIA AUTHORING AND INTERFACING TOOLS 3 rd Hellenic Conference on Artificial Intelligence, Samos, Greece, 5-8 May 2004

ONTOLOGY-BASED MULTIMEDIA AUTHORING AND INTERFACING TOOLS 3 rd Hellenic Conference on Artificial Intelligence, Samos, Greece, 5-8 May 2004 ONTOLOGY-BASED MULTIMEDIA AUTHORING AND INTERFACING TOOLS 3 rd Hellenic Conference on Artificial Intelligence, Samos, Greece, 5-8 May 2004 By Aristomenis Macris (e-mail: arism@unipi.gr), University of

More information

The Specific Text Analysis Tasks at the Beginning of MDA Life Cycle

The Specific Text Analysis Tasks at the Beginning of MDA Life Cycle SCIENTIFIC PAPERS, UNIVERSITY OF LATVIA, 2010. Vol. 757 COMPUTER SCIENCE AND INFORMATION TECHNOLOGIES 11 22 P. The Specific Text Analysis Tasks at the Beginning of MDA Life Cycle Armands Šlihte Faculty

More information

Overview of MT techniques. Malek Boualem (FT)

Overview of MT techniques. Malek Boualem (FT) Overview of MT techniques Malek Boualem (FT) This section presents an standard overview of general aspects related to machine translation with a description of different techniques: bilingual, transfer,

More information

IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper

IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper CAST-2015 provides an opportunity for researchers, academicians, scientists and

More information

Presented to The Federal Big Data Working Group Meetup On 07 June 2014 By Chuck Rehberg, CTO Semantic Insights a Division of Trigent Software

Presented to The Federal Big Data Working Group Meetup On 07 June 2014 By Chuck Rehberg, CTO Semantic Insights a Division of Trigent Software Semantic Research using Natural Language Processing at Scale; A continued look behind the scenes of Semantic Insights Research Assistant and Research Librarian Presented to The Federal Big Data Working

More information

Special Topics in Computer Science

Special Topics in Computer Science Special Topics in Computer Science NLP in a Nutshell CS492B Spring Semester 2009 Jong C. Park Computer Science Department Korea Advanced Institute of Science and Technology INTRODUCTION Jong C. Park, CS

More information

Process Mining in Big Data Scenario

Process Mining in Big Data Scenario Process Mining in Big Data Scenario Antonia Azzini, Ernesto Damiani SESAR Lab - Dipartimento di Informatica Università degli Studi di Milano, Italy antonia.azzini,ernesto.damiani@unimi.it Abstract. In

More information

Data Warehousing and Data Mining in Business Applications

Data Warehousing and Data Mining in Business Applications 133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business

More information

Combining SAWSDL, OWL DL and UDDI for Semantically Enhanced Web Service Discovery

Combining SAWSDL, OWL DL and UDDI for Semantically Enhanced Web Service Discovery Combining SAWSDL, OWL DL and UDDI for Semantically Enhanced Web Service Discovery Dimitrios Kourtesis, Iraklis Paraskakis SEERC South East European Research Centre, Greece Research centre of the University

More information

COURSE RECOMMENDER SYSTEM IN E-LEARNING

COURSE RECOMMENDER SYSTEM IN E-LEARNING International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 159-164 COURSE RECOMMENDER SYSTEM IN E-LEARNING Sunita B Aher 1, Lobo L.M.R.J. 2 1 M.E. (CSE)-II, Walchand

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them

An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them An Open Platform for Collecting Domain Specific Web Pages and Extracting Information from Them Vangelis Karkaletsis and Constantine D. Spyropoulos NCSR Demokritos, Institute of Informatics & Telecommunications,

More information

A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks

A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks Text Analytics World, Boston, 2013 Lars Hard, CTO Agenda Difficult text analytics tasks Feature extraction Bio-inspired

More information

Text Mining - Scope and Applications

Text Mining - Scope and Applications Journal of Computer Science and Applications. ISSN 2231-1270 Volume 5, Number 2 (2013), pp. 51-55 International Research Publication House http://www.irphouse.com Text Mining - Scope and Applications Miss

More information

Integrating Public and Private Medical Texts for Patient De-Identification with Apache ctakes

Integrating Public and Private Medical Texts for Patient De-Identification with Apache ctakes Integrating Public and Private Medical Texts for Patient De-Identification with Apache ctakes Presented By: Andrew McMurry & Britt Fitch (Apache ctakes committers) Co-authors: Guergana Savova, Ben Reis,

More information

Domain Knowledge Extracting in a Chinese Natural Language Interface to Databases: NChiql

Domain Knowledge Extracting in a Chinese Natural Language Interface to Databases: NChiql Domain Knowledge Extracting in a Chinese Natural Language Interface to Databases: NChiql Xiaofeng Meng 1,2, Yong Zhou 1, and Shan Wang 1 1 College of Information, Renmin University of China, Beijing 100872

More information

BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL

BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University

More information

Identify Disorders in Health Records using Conditional Random Fields and Metamap

Identify Disorders in Health Records using Conditional Random Fields and Metamap Identify Disorders in Health Records using Conditional Random Fields and Metamap AEHRC at ShARe/CLEF 2013 ehealth Evaluation Lab Task 1 G. Zuccon 1, A. Holloway 1,2, B. Koopman 1,2, A. Nguyen 1 1 The Australian

More information

Text Mining for Health Care and Medicine. Sophia Ananiadou Director National Centre for Text Mining www.nactem.ac.uk

Text Mining for Health Care and Medicine. Sophia Ananiadou Director National Centre for Text Mining www.nactem.ac.uk Text Mining for Health Care and Medicine Sophia Ananiadou Director National Centre for Text Mining www.nactem.ac.uk The Need for Text Mining MEDLINE 2005: ~14M 2009: ~18M Overwhelming information in textual,

More information

Semantic Search in Portals using Ontologies

Semantic Search in Portals using Ontologies Semantic Search in Portals using Ontologies Wallace Anacleto Pinheiro Ana Maria de C. Moura Military Institute of Engineering - IME/RJ Department of Computer Engineering - Rio de Janeiro - Brazil [awallace,anamoura]@de9.ime.eb.br

More information

Travis Goodwin & Sanda Harabagiu

Travis Goodwin & Sanda Harabagiu Automatic Generation of a Qualified Medical Knowledge Graph and its Usage for Retrieving Patient Cohorts from Electronic Medical Records Travis Goodwin & Sanda Harabagiu Human Language Technology Research

More information