FL-LIMS: Laboratory Information Management System as an intelligent system

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "FL-LIMS: Laboratory Information Management System as an intelligent system"

Transcription

1 FL-LIMS: Laboratory Information Management System as an intelligent system DIANA CALVA MENDEZ 1,2, MARIO LEHMAN 2 1 CADIT Universidad Anahuac, Av. Lomas Anáhuac s/n, Col. Lomas Anáhuac, Huixquilucan, Edo. de México, C.P , Mexico DF, MEXICO 2 CEMINT Sofilab, Lisboa 14-A, Colonia Juárez, Delegación Cuauhtémoc, México DF, MEXICO Abstract: - Laboratory Information Management Systems (LIMS) have been in use since the mid seventies. Today the laboratory needs, have more to do with decision support and decision making. Depending on the problem needed to be solved, a different and unique method of artificial intelligence would have to be implemented that could easily interact with the database search engines each LIMS uses. Here we identify some of these new problems to be solved by a LIMS, the possible alternatives that could be implemented in order to solve them and include an example based on fuzzy logic. Key-Words: - LIMS, Artificial Intelligence, Data Mining, Clinical Laboratories, Relational Databases, FSQL 1 Introduction Laboratory Information Management Systems (LIMS), support the clinical laboratory daily tasks and procedures. The "product" of a clinical laboratory is data. When data are organized in such a way as to convey meaning, they become "information". Raw laboratory data is of limited usefulness until it is transformed into information, for example, by placing it in the context of reference intervals, or through validation, and quality control measures that verify its accuracy. The clinical laboratory is well-suited to integration with medical informatics because of the large amounts of quantitative, qualitative and morphologic data generated. A typical LIMS includes the models and technological tools for managing, storing, processing, and communicating laboratory data, and for transforming it into useful information [1]. The amount of information obtained from the daily tasks performed in the clinical laboratory need to be supported with information systems. Also, most of the data collected maintain relations which need to be recorded and tracked in order to provide information useful for the laboratory operation. During the seventies and eighties, what was expected from a clinical laboratory information system, had to do only with storing and retrieving test results, booking patients, knowing the amount of tests performed over a period of time, billing patients, generating and printing test results reports, and performing simple statistics (internal quality control) among others [2]. These first systems were only known as Laboratory Information Systems (LIS), all of them were developed in-house by each clinical laboratory and depended totally on the technology available in site. It was until the beginning of the eighties when the first commercially available LIMS appeared, but were not still available to every laboratory and great amounts of money had to be invested in hardware and were based on the use of dumb terminals and proprietary databases. It was not until the beginning of the nineties when LIMS became economically available because of the appearance of personal computers and relational databases [3]. Today most LIMS use relational databases and structure query language (SQL) for performing most of these tasks which are based on data searching, updating and retrieval. As the amount of data grows and the clinical laboratory finds their new needs could, in a way, be solved by adding new functions to the LIMS, queries become more complex and the structure query language becomes limited. Most of these needs have to do with planning, logistics, aspects related with total quality requirements [4, 5], obtaining information having to do with the behavior of certain diseases (cancer) [6], the prevalence of different clinical parameters (like high glucose or cholesterol) in a target population, management of epidemic data or data that could lead to the identification of nosocomial infection (grouped by age, sex, geographical location, etc.) [7, 8], among others. Other needs have to do with including new technologies such as voice controlled tasks; image processing for identification of sample tags or character identification in medical orders; ISSN: ISBN:

2 making suggestions based on the history of a particular patient, etc [9]. Also, most of the time, the laboratory stores precise data and needs to retrieve information based on imprecise criteria where the existence of a fuzzy query language or a way of translating the stored data into imprecise information in an effective way might be the best solution for achieving this possibility. All these new needs can be solved by using one or more artificial intelligence techniques. Today, no commercial available LIMSdatabase engine provides tools based on artificial intelligence in order to perform these complex queries. In this work we identify and briefly explain some of these needs and propose a solution based on one or more artificial intelligence technique. Also we will illustrate an example of a query based on imprecise criteria over precise data. 2 Data Retrieval on a Traditional LIMS based on a Relational Database In previous works we have presented issues having to do with designing an application flexible and functional enough in order to meet different clinical laboratories needs [10], the interaction of other applications dealing with telemedicine and LIMS [11], and the particularities of the Mexican health system and how it affects LIMS design and development [12]. In the Apendix, the user interface of the LIMS, we have developed, is shown. However all previous works used commercially available relational databases and used simple queries in order to solve different problems and situations demanded from the LIMS. In relational databases, data retrieval is achieved through the performance and execution of queries, stored procedures, views or functions. Most of the time, one of these methods are enough in order to obtain results and extract useful information from the database. However these is limited for retrieving information that already exists in the database when using as search criteria any information already existing in the database as can be seen in Fig. 1, where a store procedure for obtaining the set of patients booked between certain dates is shown. The procedure retrieves the list of the patients including their First Name, Last Name and Patient ID. These data are retrieved from two different tables. However this kind of query will retrieve patients having 12 years old or less and will leave out of the result those that might still be considered as pediatric patients but whose age is slightly greater than 12 years, since the criteria pediatric is imprecise. The same will happen when trying to obtain the set of adult patients, since a range containing the ages where a patient is considered adult would have to be established, leaving out of the resulting set of patients those that might be considered as adults but do not belong to the selected range. CREATE PROCEDURE char char char varchar (50) AS = 'AF' /* ADMISION - FOLIO*/ begin SELECT C.Buscador,P.A_Paterno, P.A_Materno, P.Nom_Pac, C.Fecha_Cit,C.Folio FROM Pacientes P, Cita_Admision C WHERE C.Buscador and P.IdPac = C.IdPAc and C.Tipo = 'A' end = 'AM' /* ADMISION - MATRICULA*/ begin SELECT C.Buscador, P.A_Paterno, P.A_Materno, P.Nom_Pac, C.Fecha_Cit,C.Folio FROM Pacientes P, Cita_Admision C WHERE P.Matri_Pac and P.IdPac = C.IdPAc and C.Tipo = 'A' End Figure 1 - SQL Stored Procedure for retrieving the full name of a set of patients, by using different search criteria (data contained in a specific table of the LIMS database). SELECT Name, Age FROM Patients WHERE (Age <= 12) ORDER BY Age Name Age Laura Esquivel García 1 Mónica Hernández Gámez 3 Javier Pérez Martínez 5 Ramón López Méndez 8 Angel López Gómez 11 Helena Pérez Martínez 12 Figure 2 - Query for obtaining pediatric patients based on the assumption that these patients are the ones having 12 years old or less. ISSN: ISBN:

3 A similar query could be performed for retrieving information on a set of patients classified between a range of dates of birth, and this way the set of pediatric patients could be obtained. An example of such query can be seen in Fig Fuzzy Logic for achieving imprecise queries The description above is just a simple example of the limitations of the current solutions a LIMS based on a relational database can present. Imprecise data can more easily be obtained by using fuzzy logic. The best way to achieve fuzzy queries is by implementing fuzzy classification, this way, there is no need of having a database able to support fuzzy logic operations in the structured query language [13].This process might be achieved by converting all the crisp data into fuzzy data by using additional tables and catalogs and then applying a trapezoidal function that will allow associating a membership degree. In these case we used the following membership function: in Fig. 4. Having a fuzzy logic as a tool for retrieving imprecise data from a LIMS adds functionality and facilitates the implementation of epidemiologic applications, follow up and evaluation of tests results for a particular treatment being applied to an individual or group of individuals. NAME AGE Ulises 24 Ana 10 Carlos 55 (a) TAG a b c d Adult Young Child Old (b) Once the membership negrees are obtained for each set, we applied T-Norm, for obtaining the intersection of the sets and therefore the imprecise data needed for the classification according to age. An example of this can be seen in Fig. 3, where a table containing the membership degrees for age, are contained in table Mem_Deg_Age, this is obtained after applying f(x,a,b,c,d) to particular set of crisp data [14]. Also, the window showing the user interface of this particular application can be seen in the appendix in Fig. 5. and Fig. 6. This way, several tables containing Membership Degrees for other important classification criteria may be added as tables in a conventional database entity relation model, and fuzzy searches could then be performed even by combining the results for other different fuzzy classifications resulting from applying the corresponding function and fuzzy set operations to these new tables as can be seen (c) NAME AGE MEMBER DEGREE Ulises (young) Ana (child) Carlos (old) (d) Figure 3 - Fuzzy Classification over a conventional database. a) Table containing crisp data, b)values used for evaluating the Membership function, c) Graphical representation for obtaining the membership degree for each data d) Table containing Membership Degrees after applying f ( x, a, b, c, d) and the resulting classification after applying T-Norm ISSN: ISBN:

4 the Renyi entropy in the sense of coordinates characterizing the data [19-21]. Then, the trapezoidal membership function of Eq. (1) can also used for the case of clustering with Renyi entropy. The Renyi entropy is defined as: S R q N 1 log q = Pi 1 q (2) i= 1 where P i is a probability, which can be for the set of data, q is the free parameter which identifies the order of the entropy. Figure 4 - Scheme for data retrieval. In Fig. 4 we can see the scheme of the procedure for data retrieval, (A) SQL Query analyzer performs a query over crisp data, (B) Results from this query contain the needed information for fuzzyfication, (C, D) A particular function is applied for obtaining membership degrees, (E) Membership degrees are stored in a table, (F) After performing store procedures, triggers or views on the query analyzer fuzzy information is obtained, (G, H)Fuzzy set operations can be performed with the results obtained from the second query and an imprecise result can be obtained. Figure 5 - Window showing the result of implementing fuzzy classification, over a conventional relational database model. 4 Clustering data and fuzzification with Renyi entropies Such as was developed by other authors, using the Renyi entropy and maximizing principle, we can use Figure 6 Clusters with Renyi entropy, for two different values of q. 5 Conclusion The application of artificial intelligence techniques is proposed for performing several functions that, today result difficult to develop with simple SQL queries. We show an example of how imprecise queries that are useful for the ISSN: ISBN:

5 clinical laboratory can be achieved. Future work involves evaluating the different artificial intelligence techniques, their implementation with simple SQL queries and which are best suited for adding functionality to current LIMS which do not count with these kind of applications and tools. As additional result we present a scheme based on the Renyi entropy for clusterizing the data distribution and build fuzzy clusters, which can be included in the LIMS for retrieval information and classification. Acknowledgments This work was supported by Sofilab S.A. de C.V. and Fondo Sectorial para el Desarrollo Económico, from Consejo Nacional de Ciencia y Tecnología (Conacyt) and SE (México). References: [1] Buffone, G. J., Dennis, R. M.,: Laboratory Computing-Process and Information Management Supporting High-Quality, Cost-Effective healthcare. Clinical Chemistry. 41(9), (1995). [2] Cristopher, M.M., Hotz, CH.S.: Medical Informatics in the Veterinary Clinical Laboratory: an Integrated Subspecialty in Diagnostics, Resident Training and Clinical Research. Revue De Medecine Veterinaire. 152(7), (2000). [3] Townsend, N., Waugh, M., Flatter, M.: LIMS: Meeting the challenge of modern business. American Laboratory. March, (2001). [4] Grauer, Z.: Laboratory Information Management Systems and Traceability of Quality Systems. American Laboratory. September, (2003). [5] Steele, T.W., Laugier, A., Falco, F.; The Impact of LIMS Design and Functionality on Laboratory Quality Achievements. Accreditation and Quality Assurance: Journal for Quality, Comparability and Reliability in Chemical Measurement. 4(3), [6] Quo, C.F., Wu, B., Wang, M.D.: Development of a Laboratory Information System for Cancer Collaboration Projects. Annual International Conference of the IEEE-EMBS th, (2005). [7] Wurtz, R., Cameron, B.J.: Electronic Laboratory Reporting for the Infectious Diseases Physician and Clinical Microbiologist. Clinical Infectious Diseases. 40, (2005). [8] Lamma, E., Mello, P, Nanetti. A, Riguzzi. F, Storari. S, Valastro. G: Artificial Intelligence Techniques for Monitoring Dangerous Infections. IEEE Transactions on Information Technology in Biomediciene. Jan:10(1), (2006). [9] Winkel, P.: The Application of Expert Systems in the Clinical Laboratory. Clinical Chemistry. 35, (1989). [10] Calva, D., Landa, A., Lehman, M.: Information Management in the Clinical Laboratory. WSEAS Transactions on Computers. 5(3), 1238 (2004). [11] Calva, D., Landa, A., Lehman, M.: A Low Cost System for Telepathology. WSEAS Transactions on Biology and Biomedicine 2(2) 253 (2005). [12] Landa, A., Calva, D., Lehman, M.: Sistemas de Información para la Administración del Laboratorio Clínico. Proceedings del congreso de SOMIB (2003). [13] Werro, N., Meier, C., Mezger, C., Schindler, G.: Concept and Implementation of a Fuzzy Classification Query Language. Proceedings of the International Conference on Data Mining, DMIN 05, World Congress in Applied Computing, Las Vegas (2005). [14] Medina, J.M., Vila, M.A., Cubero, J.C., Pons, O.: Towards the Implementation of a Generalized Fuzzy Relational Database Model. Fuzzy Sets and Systems. 75(10), (1995). [15] Kononenko, I.: Machine Learning for Medical Diagnosis: History, State of the Art and Perspective. Artificial Intelligence in Medicine. 23(1), (2001). [16] Westgard, J.O., Barry, P.L., Hunt, M.R., Groth, T.: Clinical Chemistry. 27(3), (1981). [17] Chang, J.Y., Tao, S.Q., Deng, CH.: Design and Implementation of a Deductive Database System, J. Beijing Polytech. Univ. 30(4), (2004). [18] Taskar, B., Segal, E. Killer, D.: Probabilistic Classification and Clustering in Relational Data. Proceeding of IJCAI-01, 17 th International Joint Conference on Artificial Intelligence. [19] E. Gokcay, J. C. Principe, A New Clustering Evaluation Function Using Renyi's Information Potential, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), [20] R. Jenssen, K. E. Hild, D. Erdogmus, J. C. Principe, T. Eltoft, T., Clustering using Renyi s entropy, Neural Networks, Proceedings of the International Joint Conference, Volume 1, Issue, July 2003 Page(s): [21] M. M. Mayoral, Renyi's entropy as an index of diversity in simple-stage cluster sampling, Information Sciences, Volume 105, Number 1, March 1998, pp (14). ISSN: ISBN:

Health Condition Alarm System

Health Condition Alarm System Health Condition Alarm System Maiga Chang 1, Ebenezer Aggrey 1, Mehadi Sayed 2, and Kinshuk 1 1 School of Computing and Information Systems, Athabasca University, Canada maiga@ms2.hinet.net, aggreyeb@shaw.ca,

More information

Master in Science, specialty in Information Systems. ITESM. México, June 1984.

Master in Science, specialty in Information Systems. ITESM. México, June 1984. LAURA CRUZ REYES Instituto Tecnológico de Cd. Madero División de Estudios de Posgrado e Investigación Juventino Rosas y Jesús Urueta Cd. Madero Tamaulipas México CP. 89440 Tel/Fax (52) 833 215 85 44 e-mail:

More information

International Journal of Electronics and Computer Science Engineering 1449

International Journal of Electronics and Computer Science Engineering 1449 International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and

More information

Supporting Competence upon DotLRN through Personalization

Supporting Competence upon DotLRN through Personalization Supporting Competence upon DotLRN through Personalization Carolina Mejía, Laura Mancera, Sergio Gómez, Silvia Balidiris, Ramón Fabregat University of Girona, Institute of Informatics Applications, 17071

More information

On Development of Fuzzy Relational Database Applications

On Development of Fuzzy Relational Database Applications On Development of Fuzzy Relational Database Applications Srdjan Skrbic Faculty of Science Trg Dositeja Obradovica 3 21000 Novi Sad Serbia shkrba@uns.ns.ac.yu Aleksandar Takači Faculty of Technology Bulevar

More information

Pattern Recognition in Medical Images using Neural Networks. 1. Pattern Recognition and Neural Networks

Pattern Recognition in Medical Images using Neural Networks. 1. Pattern Recognition and Neural Networks Pattern Recognition in Medical Images using Neural Networks Lic. Laura Lanzarini 1, Ing. A. De Giusti 2 Laboratorio de Investigación y Desarrollo en Informática 3 Departamento de Informática - Facultad

More information

A MECHATRONICS APPROACH IN INTELLIGENT CONTROL SYSTEMS OF THE OVERHEAD TRAVELING CRANES PROTOTYPING

A MECHATRONICS APPROACH IN INTELLIGENT CONTROL SYSTEMS OF THE OVERHEAD TRAVELING CRANES PROTOTYPING ISSN 1392 124X INFORMATION TECHNOLOGY AND CONTROL, 2008, Vol.37, No.2 A MECHATRONICS APPROACH IN INTELLIGENT CONTROL SYSTEMS OF THE OVERHEAD TRAVELING CRANES PROTOTYPING Jarosław Smocze, Janusz Szpyto

More information

IMPROVING PIPELINE RISK MODELS BY USING DATA MINING TECHNIQUES

IMPROVING PIPELINE RISK MODELS BY USING DATA MINING TECHNIQUES IMPROVING PIPELINE RISK MODELS BY USING DATA MINING TECHNIQUES María Fernanda D Atri 1, Darío Rodriguez 2, Ramón García-Martínez 2,3 1. MetroGAS S.A. Argentina. 2. Área Ingeniería del Software. Licenciatura

More information

Master of Science in Healthcare Informatics and Analytics Program Overview

Master of Science in Healthcare Informatics and Analytics Program Overview Master of Science in Healthcare Informatics and Analytics Program Overview The program is a 60 credit, 100 week course of study that is designed to graduate students who: Understand and can apply the appropriate

More information

Clinical Database Information System for Gbagada General Hospital

Clinical Database Information System for Gbagada General Hospital International Journal of Research Studies in Computer Science and Engineering (IJRSCSE) Volume 2, Issue 9, September 2015, PP 29-37 ISSN 2349-4840 (Print) & ISSN 2349-4859 (Online) www.arcjournals.org

More information

Contents. Abstract...i. Committee Membership... iii. Foreword... vii. 1 Scope...1

Contents. Abstract...i. Committee Membership... iii. Foreword... vii. 1 Scope...1 ISBN 1-56238-584-4 Volume 25 Number 27 ISSN 0273-3099 Interference Testing in Clinical Chemistry; Approved Guideline Second Edition Robert J. McEnroe, PhD Mary F. Burritt, PhD Donald M. Powers, PhD Douglas

More information

Intelligent Systems to Assist in Cytological Diagnosis and to Train Cytotechnics TIN2004-07236

Intelligent Systems to Assist in Cytological Diagnosis and to Train Cytotechnics TIN2004-07236 Jornadas de Seguimiento de Proyectos, 2007 Programa Nacional de Tecnologías Informáticas Intelligent Systems to Assist in Cytological Diagnosis and to Train Cytotechnics TIN2004-07236 Jose Manuel Zurita

More information

A Framework for Data Warehouse Using Data Mining and Knowledge Discovery for a Network of Hospitals in Pakistan

A Framework for Data Warehouse Using Data Mining and Knowledge Discovery for a Network of Hospitals in Pakistan , pp.217-222 http://dx.doi.org/10.14257/ijbsbt.2015.7.3.23 A Framework for Data Warehouse Using Data Mining and Knowledge Discovery for a Network of Hospitals in Pakistan Muhammad Arif 1,2, Asad Khatak

More information

EVALUATION FRAMEWORK FOR SERVICE CATALOG MATURITY IN INFORMATION TECHNOLOGY ORGANIZATIONS

EVALUATION FRAMEWORK FOR SERVICE CATALOG MATURITY IN INFORMATION TECHNOLOGY ORGANIZATIONS EVALUATION FRAMEWORK FOR SERVICE CATALOG MATURITY IN INFORMATION TECHNOLOGY ORGANIZATIONS Carlos Moreno Martínez Information Systems Department, Universidad Europea de Madrid Spain Email: 20839394@live.uem.es

More information

Prediction of Heart Disease Using Naïve Bayes Algorithm

Prediction of Heart Disease Using Naïve Bayes Algorithm Prediction of Heart Disease Using Naïve Bayes Algorithm R.Karthiyayini 1, S.Chithaara 2 Assistant Professor, Department of computer Applications, Anna University, BIT campus, Tiruchirapalli, Tamilnadu,

More information

Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network

Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network General Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Impelling

More information

LABERINTO at ImageCLEF 2011 Medical Image Retrieval Task

LABERINTO at ImageCLEF 2011 Medical Image Retrieval Task LABERINTO at ImageCLEF 2011 Medical Image Retrieval Task Jacinto Mata, Mariano Crespo, Manuel J. Maña Dpto. de Tecnologías de la Información. Universidad de Huelva Ctra. Huelva - Palos de la Frontera s/n.

More information

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014 RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

More information

AN RFID AND MULTI-AGENT BASED SYSTEM ENABLING ACCESS TO PATIENT MEDICAL HISTORY

AN RFID AND MULTI-AGENT BASED SYSTEM ENABLING ACCESS TO PATIENT MEDICAL HISTORY AN RFID AND MULTI-AGENT BASED SYSTEM ENABLING ACCESS TO PATIENT MEDICAL HISTORY Felicia Giza-Belciug 1, Cristina Turcu 2 and Cornel Turcu 3 1 Department of Electrical Engineering and Computer Science,

More information

Grupo de Ingeniería de Sistemas Integrados (ISIS) Dpto. Tecnología Electrónica Universidad de Málaga

Grupo de Ingeniería de Sistemas Integrados (ISIS) Dpto. Tecnología Electrónica Universidad de Málaga Grupo de Ingeniería de Sistemas Integrados (ISIS) Dpto. Tecnología Electrónica Universidad de Málaga FP7-ENERGY: Oportunidades de Financiación Europea de la I+D+i y de Liderazgo para empresas y grupos

More information

Mathematical models to estimate the quality of monitoring software systems for electrical substations

Mathematical models to estimate the quality of monitoring software systems for electrical substations Mathematical models to estimate the quality of monitoring software systems for electrical substations MIHAIELA ILIESCU 1, VICTOR URSIANU 2, FLORICA MOLDOVEANU 2, RADU URSIANU 2, EMILIANA URSIANU 3 1 Faculty

More information

Healthcare Measurement Analysis Using Data mining Techniques

Healthcare Measurement Analysis Using Data mining Techniques www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik

More information

KNOWLEDGE-BASED IN MEDICAL DECISION SUPPORT SYSTEM BASED ON SUBJECTIVE INTELLIGENCE

KNOWLEDGE-BASED IN MEDICAL DECISION SUPPORT SYSTEM BASED ON SUBJECTIVE INTELLIGENCE JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 22/2013, ISSN 1642-6037 medical diagnosis, ontology, subjective intelligence, reasoning, fuzzy rules Hamido FUJITA 1 KNOWLEDGE-BASED IN MEDICAL DECISION

More information

A Comparison of Calibrated Equations for Software Development Effort Estimation

A Comparison of Calibrated Equations for Software Development Effort Estimation A Comparison of Calibrated Equations for Software Development Effort Estimation Cuauhtemoc Lopez Martin Edgardo Felipe Riveron Agustin Gutierrez Tornes 3,, 3 Center for Computing Research, National Polytechnic

More information

Mobile Access to Patient Clinical Records and Related Medical Documentation

Mobile Access to Patient Clinical Records and Related Medical Documentation Mobile Access to Patient Clinical Records and Related Medical Documentation Diego Gachet, Manuel de Buenaga, Enrique Puertas Universidad Europea de Madrid Escuela Superior Politécnica 28670 Villaviciosa

More information

Improving Decision Making and Managing Knowledge

Improving Decision Making and Managing Knowledge Improving Decision Making and Managing Knowledge Decision Making and Information Systems Information Requirements of Key Decision-Making Groups in a Firm Senior managers, middle managers, operational managers,

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

Establishing the Uniqueness of the Human Voice for Security Applications

Establishing the Uniqueness of the Human Voice for Security Applications Proceedings of Student/Faculty Research Day, CSIS, Pace University, May 7th, 2004 Establishing the Uniqueness of the Human Voice for Security Applications Naresh P. Trilok, Sung-Hyuk Cha, and Charles C.

More information

DESKTOP BASED RECOMMENDATION SYSTEM FOR CAMPUS RECRUITMENT USING MAHOUT

DESKTOP BASED RECOMMENDATION SYSTEM FOR CAMPUS RECRUITMENT USING MAHOUT Journal homepage: www.mjret.in ISSN:2348-6953 DESKTOP BASED RECOMMENDATION SYSTEM FOR CAMPUS RECRUITMENT USING MAHOUT 1 Ronak V Patil, 2 Sneha R Gadekar, 3 Prashant P Chavan, 4 Vikas G Aher Department

More information

In Proceedings of the Eleventh Conference on Biocybernetics and Biomedical Engineering, pages 842-846, Warsaw, Poland, December 2-4, 1999

In Proceedings of the Eleventh Conference on Biocybernetics and Biomedical Engineering, pages 842-846, Warsaw, Poland, December 2-4, 1999 In Proceedings of the Eleventh Conference on Biocybernetics and Biomedical Engineering, pages 842-846, Warsaw, Poland, December 2-4, 1999 A Bayesian Network Model for Diagnosis of Liver Disorders Agnieszka

More information

Forecasting Stock Prices using a Weightless Neural Network. Nontokozo Mpofu

Forecasting Stock Prices using a Weightless Neural Network. Nontokozo Mpofu Forecasting Stock Prices using a Weightless Neural Network Nontokozo Mpofu Abstract In this research work, we propose forecasting stock prices in the stock market industry in Zimbabwe using a Weightless

More information

Degree of Uncontrollable External Factors Impacting to NPD

Degree of Uncontrollable External Factors Impacting to NPD Degree of Uncontrollable External Factors Impacting to NPD Seonmuk Park, 1 Jongseong Kim, 1 Se Won Lee, 2 Hoo-Gon Choi 1, * 1 Department of Industrial Engineering Sungkyunkwan University, Suwon 440-746,

More information

DESIGN AND IMPLEMENTATION OF A WEB-BASED GIS FOR PATIENTS REFERRAL TO HOSPITALS IN ZARIA METROPOLIS

DESIGN AND IMPLEMENTATION OF A WEB-BASED GIS FOR PATIENTS REFERRAL TO HOSPITALS IN ZARIA METROPOLIS www.arpapress.com/volumes/vol8issue1/ijrras_8_1_14.pdf DESIGN AND IMPLEMENTATION OF A WEB-BASED GIS FOR PATIENTS REFERRAL TO HOSPITALS IN ZARIA METROPOLIS F.B Abdullahi 1 & T.Hassan 2 Department of Mathematics

More information

Linguistic Preference Modeling: Foundation Models and New Trends. Extended Abstract

Linguistic Preference Modeling: Foundation Models and New Trends. Extended Abstract Linguistic Preference Modeling: Foundation Models and New Trends F. Herrera, E. Herrera-Viedma Dept. of Computer Science and Artificial Intelligence University of Granada, 18071 - Granada, Spain e-mail:

More information

Volume 2, Issue 12, December 2014 International Journal of Advance Research in Computer Science and Management Studies

Volume 2, Issue 12, December 2014 International Journal of Advance Research in Computer Science and Management Studies Volume 2, Issue 12, December 2014 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com

More information

A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE

A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE Kasra Madadipouya 1 1 Department of Computing and Science, Asia Pacific University of Technology & Innovation ABSTRACT Today, enormous amount of data

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

DIFFERENTIATIONS OF OBJECTS IN DIFFUSE DATABASES DIFERENCIACIONES DE OBJETOS EN BASES DE DATOS DIFUSAS

DIFFERENTIATIONS OF OBJECTS IN DIFFUSE DATABASES DIFERENCIACIONES DE OBJETOS EN BASES DE DATOS DIFUSAS Recibido: 01 de agosto de 2012 Aceptado: 23 de octubre de 2012 DIFFERENTIATIONS OF OBJECTS IN DIFFUSE DATABASES DIFERENCIACIONES DE OBJETOS EN BASES DE DATOS DIFUSAS PhD. Amaury Caballero*; PhD. Gabriel

More information

Evaluation of an exercise for measuring impact in e-learning: Case study of learning a second language

Evaluation of an exercise for measuring impact in e-learning: Case study of learning a second language Evaluation of an exercise for measuring impact in e-learning: Case study of learning a second language J.M. Sánchez-Torres Universidad Nacional de Colombia Bogotá D.C., Colombia jmsanchezt@unal.edu.co

More information

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016

Network Machine Learning Research Group. Intended status: Informational October 19, 2015 Expires: April 21, 2016 Network Machine Learning Research Group S. Jiang Internet-Draft Huawei Technologies Co., Ltd Intended status: Informational October 19, 2015 Expires: April 21, 2016 Abstract Network Machine Learning draft-jiang-nmlrg-network-machine-learning-00

More information

Validation of measurement procedures

Validation of measurement procedures Validation of measurement procedures R. Haeckel and I.Püntmann Zentralkrankenhaus Bremen The new ISO standard 15189 which has already been accepted by most nations will soon become the basis for accreditation

More information

Design of a Decision Support System in Electronic Medical Record Using Structured Query Language

Design of a Decision Support System in Electronic Medical Record Using Structured Query Language DOI: 10.7763/IPEDR. 2013. V63. 3 Design of a Decision Support System in Electronic Medical Record Using Structured Query Language Muhammad Asif +, Mohammad Jamil Sawar, and Umair Abdullah Barani Institute

More information

SAMPLE. Verification of Comparability of Patient Results Within One Health Care System; Approved Guideline (Interim Revision)

SAMPLE. Verification of Comparability of Patient Results Within One Health Care System; Approved Guideline (Interim Revision) August 2012 Verification of Comparability of Patient Results Within One Health Care System; Approved Guideline (Interim Revision) This document provides guidance on how to verify comparability of quantitative

More information

Abstract 015-0145 CHALLENGES IN THE BUSINESS INCUBATION IN JALISCO, MEXICO. Dr. AIDA LUCIA FAJARDO MONTIEL, Universidad de Guadalajara Departamento

Abstract 015-0145 CHALLENGES IN THE BUSINESS INCUBATION IN JALISCO, MEXICO. Dr. AIDA LUCIA FAJARDO MONTIEL, Universidad de Guadalajara Departamento Abstract 015-0145 CHALLENGES IN THE BUSINESS INCUBATION IN JALISCO, MEXICO. Dr. AIDA LUCIA FAJARDO MONTIEL, Universidad de Guadalajara Departamento de Ingeniería Industrial, Av. Revolución 1500 Modulo

More information

System for Dynamically Sharing Real-Time Biological Signal in Any Place, in Any Device

System for Dynamically Sharing Real-Time Biological Signal in Any Place, in Any Device System for Dynamically Sharing Real-Time Biological Signal in Any Place, in Any Device Hee Kyong Park, Soo Young Yoo, Bo Young Kim, Jin Wook Choi Department of Biomedical Engineering, College of Medicine,

More information

Curriculum Vitae. Andrés Felipe Serna Morales. Personal Details. Address

Curriculum Vitae. Andrés Felipe Serna Morales. Personal Details. Address Curriculum Vitae Andrés Felipe Serna Morales Personal Details Name: Andrés Felipe Serna Morales Genre: Male Birth date: April 6, 1987 Birth Place: Manizales, Caldas, Colombia Nationality: Colombian Status:

More information

Agent Based Decision Support System for Identifying the Spread of Nosocomial Infections in a Rural Hospital

Agent Based Decision Support System for Identifying the Spread of Nosocomial Infections in a Rural Hospital Agent Based Decision Support System for Identifying the Spread of Nosocomial s in a Rural Hospital Nisha Wilvicta.J*, E.Grace Mary Kanaga** *(Department of Computer Science, Karunya University, Coimbatore-114)

More information

National Cancer Institute

National Cancer Institute National Cancer Institute Information Systems, Technology, and Dissemination in the SEER Program U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Information Systems, Technology,

More information

testo dello schema Secondo livello Terzo livello Quarto livello Quinto livello

testo dello schema Secondo livello Terzo livello Quarto livello Quinto livello Extracting Knowledge from Biomedical Data through Logic Learning Machines and Rulex Marco Muselli Institute of Electronics, Computer and Telecommunication Engineering National Research Council of Italy,

More information

Master of Science in Health Information Technology Degree Curriculum

Master of Science in Health Information Technology Degree Curriculum Master of Science in Health Information Technology Degree Curriculum Core courses: 8 courses Total Credit from Core Courses = 24 Core Courses Course Name HRS Pre-Req Choose MIS 525 or CIS 564: 1 MIS 525

More information

Fault Analysis in Software with the Data Interaction of Classes

Fault Analysis in Software with the Data Interaction of Classes , pp.189-196 http://dx.doi.org/10.14257/ijsia.2015.9.9.17 Fault Analysis in Software with the Data Interaction of Classes Yan Xiaobo 1 and Wang Yichen 2 1 Science & Technology on Reliability & Environmental

More information

Topic: A Personal Data Audit Method through Requirements Engineering. Method Description. Lucian Cancescu - 3609707 Group 1

Topic: A Personal Data Audit Method through Requirements Engineering. Method Description. Lucian Cancescu - 3609707 Group 1 Topic: A Personal Data Audit Method through Requirements Engineering Method Description Lucian Cancescu - 3609707 Group 1 1. Introduction The Personal Data Autit Method based on Requirements Engineering

More information

A Bayesian Network Model for Diagnosis of Liver Disorders Agnieszka Onisko, M.S., 1,2 Marek J. Druzdzel, Ph.D., 1 and Hanna Wasyluk, M.D.,Ph.D.

A Bayesian Network Model for Diagnosis of Liver Disorders Agnieszka Onisko, M.S., 1,2 Marek J. Druzdzel, Ph.D., 1 and Hanna Wasyluk, M.D.,Ph.D. Research Report CBMI-99-27, Center for Biomedical Informatics, University of Pittsburgh, September 1999 A Bayesian Network Model for Diagnosis of Liver Disorders Agnieszka Onisko, M.S., 1,2 Marek J. Druzdzel,

More information

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features Remote Sensing and Geoinformation Lena Halounová, Editor not only for Scientific Cooperation EARSeL, 2011 Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with

More information

Problem-Centered Care Delivery

Problem-Centered Care Delivery HOW INTERFACE TERMINOLOGY MAKES STANDARDIZED HEALTH INFORMATION POSSIBLE Terminologies ensure that the languages of medicine can be understood by both humans and machines. by June Bronnert, RHIA, CCS,

More information

Neural Networks in Data Mining

Neural Networks in Data Mining IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V6 PP 01-06 www.iosrjen.org Neural Networks in Data Mining Ripundeep Singh Gill, Ashima Department

More information

INVOICE REQUIREMENTS. The following data must appear in the invoice for its correct payment processing: Procter & Gamble information LE 376

INVOICE REQUIREMENTS. The following data must appear in the invoice for its correct payment processing: Procter & Gamble information LE 376 INVOICE REQUIREMENTS The following data must appear in the invoice for its correct payment processing: Procter & Gamble information LE 376 1- P&G Legal entity: COMPAÑIA PROCTER & GAMBLE MEXICO S DE R.L.

More information

ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS

ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS ENHANCED CONFIDENCE INTERPRETATIONS OF GP BASED ENSEMBLE MODELING RESULTS Michael Affenzeller (a), Stephan M. Winkler (b), Stefan Forstenlechner (c), Gabriel Kronberger (d), Michael Kommenda (e), Stefan

More information

B. Clinical Data Management

B. Clinical Data Management B. Clinical Data Management The purpose of the applications of this group is to support the clinical needs of care providers including maintaining accurate medical records. Ideally, a clinical data management

More information

medexter clinical decision support

medexter clinical decision support medexter Arden Syntax training course Klaus-Peter Adlassnig and Karsten Fehre Medexter Healthcare Borschkegasse 7/5 A-1090 Vienna www.medexter.com Arden Syntax training course, Vienna, 17 June 2015 Computers

More information

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,

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

Design of the Database of Library Information

Design of the Database of Library Information Design of the Database of Library Information XueLian Feng and HaiYan Liu Baotou railway vocational technical college,baotou,inner Mongolia, 014040,China snow_snowflake@sina.com.cn, liuhaiyanliuhaiyan@live.cn

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

Study and Analysis of Data Mining Concepts

Study and Analysis of Data Mining Concepts Study and Analysis of Data Mining Concepts M.Parvathi Head/Department of Computer Applications Senthamarai college of Arts and Science,Madurai,TamilNadu,India/ Dr. S.Thabasu Kannan Principal Pannai College

More information

NOVA CELLS OF MEXICO US OFFICE: 401 S. Harbor Blvd. Suite F 179 La Habra, Ca 90631 NCInfodesk@gmail.com 1-562-916-3410

NOVA CELLS OF MEXICO US OFFICE: 401 S. Harbor Blvd. Suite F 179 La Habra, Ca 90631 NCInfodesk@gmail.com 1-562-916-3410 WWW.NOVACELLSINSTITUTE.COM NOVA CELLS OF MEXICO US OFFICE: 401 S. Harbor Blvd. Suite F 179 La Habra, Ca 90631 NCInfodesk@gmail.com 1-562-916-3410 Nova Cells Participating Doctors & Other Professionals

More information

APPENDIX to http://dx.doi.org/10.4338/aci-2014-09-ra-0083 CAHIIM 2012 Curriculum Requirements Health Informatics Master s Degree

APPENDIX to http://dx.doi.org/10.4338/aci-2014-09-ra-0083 CAHIIM 2012 Curriculum Requirements Health Informatics Master s Degree APPENDIX to http://dx.doi.org/10.4338/aci-2014-09-ra-0083 CAHIIM 2012 Curriculum Requirements Health Informatics Master s Degree Column 1 - Health Informatics Facet I. Information Systems concerned with

More information

Decision Support System In Heart Disease Diagnosis By Case Based Recommendation

Decision Support System In Heart Disease Diagnosis By Case Based Recommendation Decision Support System In Heart Disease Diagnosis By Case Based Recommendation Prinsha Prakash Abstract: Heart disease is the main leading killer as well as a major cause of disability. Its timely detection

More information

IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES

IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES IDENTIFYING BANK FRAUDS USING CRISP-DM AND DECISION TREES Bruno Carneiro da Rocha 1,2 and Rafael Timóteo de Sousa Júnior 2 1 Bank of Brazil, Brasília-DF, Brazil brunorocha_33@hotmail.com 2 Network Engineering

More information

IDENTIFIC ATION OF SOFTWARE EROSION USING LOGISTIC REGRESSION

IDENTIFIC ATION OF SOFTWARE EROSION USING LOGISTIC REGRESSION http:// IDENTIFIC ATION OF SOFTWARE EROSION USING LOGISTIC REGRESSION Harinder Kaur 1, Raveen Bajwa 2 1 PG Student., CSE., Baba Banda Singh Bahadur Engg. College, Fatehgarh Sahib, (India) 2 Asstt. Prof.,

More information

INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY DATA MINING IN HEALTHCARE SECTOR. ankitanandurkar2394@gmail.com

INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY DATA MINING IN HEALTHCARE SECTOR. ankitanandurkar2394@gmail.com IJFEAT INTERNATIONAL JOURNAL FOR ENGINEERING APPLICATIONS AND TECHNOLOGY DATA MINING IN HEALTHCARE SECTOR Bharti S. Takey 1, Ankita N. Nandurkar 2,Ashwini A. Khobragade 3,Pooja G. Jaiswal 4,Swapnil R.

More information

Maintenance of Domain Knowledge for Nursing Care using Data in Hospital Information System

Maintenance of Domain Knowledge for Nursing Care using Data in Hospital Information System Maintenance of Domain Knowledge for Nursing Care using Data in Hospital Information System Haruko Iwata, Shoji Hirano and Shusaku Tsumoto Department of Medical Informatics, School of Medicine, Faculty

More information

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION

ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION ISSN 9 X INFORMATION TECHNOLOGY AND CONTROL, 00, Vol., No.A ON INTEGRATING UNSUPERVISED AND SUPERVISED CLASSIFICATION FOR CREDIT RISK EVALUATION Danuta Zakrzewska Institute of Computer Science, Technical

More information

CRM techniques for analyzing client profiles in tourism promotion web sites

CRM techniques for analyzing client profiles in tourism promotion web sites CRM techniques for analyzing client profiles in tourism promotion web sites Abstract José L. Caro a, Antonio Carrillo a, Andrés Aguayo a, Sergio Gálvez a and Antonio Guevara a, a Lenguajes y Ciencias de

More information

A Web-based Interactive Data Visualization System for Outlier Subspace Analysis

A Web-based Interactive Data Visualization System for Outlier Subspace Analysis A Web-based Interactive Data Visualization System for Outlier Subspace Analysis Dong Liu, Qigang Gao Computer Science Dalhousie University Halifax, NS, B3H 1W5 Canada dongl@cs.dal.ca qggao@cs.dal.ca Hai

More information

FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM

FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT

More information

Data Mining for Knowledge Management in Technology Enhanced Learning

Data Mining for Knowledge Management in Technology Enhanced Learning Proceedings of the 6th WSEAS International Conference on Applications of Electrical Engineering, Istanbul, Turkey, May 27-29, 2007 115 Data Mining for Knowledge Management in Technology Enhanced Learning

More information

Presence Suite helps Arriva Medical Triple their Contact Center Productivity

Presence Suite helps Arriva Medical Triple their Contact Center Productivity Presence Suite helps Arriva Medical Triple their Contact Center Productivity Executive summary Customer name: Arriva Medical Industry: Business Services Location: United State of America Key Facts for

More information

Employee Likelihood of Purchasing Health Insurance using Fuzzy Inference System

Employee Likelihood of Purchasing Health Insurance using Fuzzy Inference System www.ijcsi.org 112 Employee Likelihood of Purchasing Health Insurance using Fuzzy Inference System Lazim Abdullah 1 and Mohd Nordin Abd Rahman 2 1 Department of Mathematics, University Malaysia Terengganu,

More information

The Big Data mining to improve medical diagnostics quality

The Big Data mining to improve medical diagnostics quality The Big Data mining to improve medical diagnostics quality Ilyasova N.Yu., Kupriyanov A.V. Samara State Aerospace University, Image Processing Systems Institute, Russian Academy of Sciences Abstract. The

More information

Credit Card Fraud Detection Using Self Organised Map

Credit Card Fraud Detection Using Self Organised Map International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 13 (2014), pp. 1343-1348 International Research Publications House http://www. irphouse.com Credit Card Fraud

More information

Objectives. ICD Background. Introduction to ICD-10 and what you need to know for a Successful Transition

Objectives. ICD Background. Introduction to ICD-10 and what you need to know for a Successful Transition Introduction to ICD-10 and what you need to know for a Successful Transition Sheila Goethel, RHIT, CCS Coding Consultant AHIMA ICD-10-CM/PCS Certified Trainer May 2011 Objectives Introduction of ICD Brief

More information

BI en Salud: Registro de Salud Electrónico, Estado del Arte!

BI en Salud: Registro de Salud Electrónico, Estado del Arte! BI en Salud: Registro de Salud Electrónico, Estado del Arte! Manuel Graña Romay! ENGINE Centre, Wrocław University of Technology! Grupo de Inteligencia Computacional (GIC); UPV/EHU; www.ehu.es/ ccwintco!

More information

Telling the Data Story: Use of Informatics, Harmonized Semantics and Metadata in the National Children s Study

Telling the Data Story: Use of Informatics, Harmonized Semantics and Metadata in the National Children s Study Telling the Data Story: Use of Informatics, Harmonized Semantics and Metadata in the National Children s Study John Lumpkin, MS, MBA, PMP Steven Hirschfeld, MD, PhD NIH-NICHD-National Children s Study

More information

Application of Syndication to the Management of Bibliographic Catalogs

Application of Syndication to the Management of Bibliographic Catalogs Journal of Computer Science 8 (3): 425-430, 2012 ISSN 1549-3636 2012 Science Publications Application of Syndication to the Management of Bibliographic Catalogs Manuel Blazquez Ochando and Juan-Antonio

More information

HMLV Manufacturing Systems Simulation Analysis Using the Database Interface

HMLV Manufacturing Systems Simulation Analysis Using the Database Interface HMLV Manufacturing Systems Simulation Analysis Using the Database Interface JURAJ ŠVANČARA Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Ilkovicova

More information

A FUZZY LOGIC APPROACH FOR SALES FORECASTING

A FUZZY LOGIC APPROACH FOR SALES FORECASTING A FUZZY LOGIC APPROACH FOR SALES FORECASTING ABSTRACT Sales forecasting proved to be very important in marketing where managers need to learn from historical data. Many methods have become available for

More information

Keywords data mining, prediction techniques, decision making.

Keywords data mining, prediction techniques, decision making. Volume 5, Issue 4, April 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Datamining

More information

Introduction to Information and Computer Science: Information Systems

Introduction to Information and Computer Science: Information Systems Introduction to Information and Computer Science: Information Systems Lecture 1 Audio Transcript Slide 1 Welcome to Introduction to Information and Computer Science: Information Systems. The component,

More information

Proposed Electronic Medical Record with Emphasis on Hepatitis Diagnosis

Proposed Electronic Medical Record with Emphasis on Hepatitis Diagnosis 138 S P E C I A L P A P E R. Proposed Electronic Medical Record with Emphasis on Hepatitis Diagnosis Anna Tsakona, RN, BSc, MSc, PhD (c) Department of Computer Engineering and Informatics, University of

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

PREDICTING STOCK PRICES USING DATA MINING TECHNIQUES

PREDICTING STOCK PRICES USING DATA MINING TECHNIQUES The International Arab Conference on Information Technology (ACIT 2013) PREDICTING STOCK PRICES USING DATA MINING TECHNIQUES 1 QASEM A. AL-RADAIDEH, 2 ADEL ABU ASSAF 3 EMAN ALNAGI 1 Department of Computer

More information

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin

Data Mining for Customer Service Support. Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Data Mining for Customer Service Support Senioritis Seminar Presentation Megan Boice Jay Carter Nick Linke KC Tobin Traditional Hotline Services Problem Traditional Customer Service Support (manufacturing)

More information

Software project cost estimation using AI techniques

Software project cost estimation using AI techniques Software project cost estimation using AI techniques Rodríguez Montequín, V.; Villanueva Balsera, J.; Alba González, C.; Martínez Huerta, G. Project Management Area University of Oviedo C/Independencia

More information

Using Data Mining for Mobile Communication Clustering and Characterization

Using Data Mining for Mobile Communication Clustering and Characterization Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer

More information

THE INTELLIGENT INTERFACE FOR ON-LINE ELECTRONIC MEDICAL RECORDS USING TEMPORAL DATA MINING

THE INTELLIGENT INTERFACE FOR ON-LINE ELECTRONIC MEDICAL RECORDS USING TEMPORAL DATA MINING International Journal of Hybrid Computational Intelligence Volume 4 Numbers 1-2 January-December 2011 pp. 1-5 THE INTELLIGENT INTERFACE FOR ON-LINE ELECTRONIC MEDICAL RECORDS USING TEMPORAL DATA MINING

More information

Appendix G STATISTICAL METHODS INFECTIOUS METHODS STATISTICAL ROADMAP. Prepared in Support of: CDC/NCEH Cross Sectional Assessment Study.

Appendix G STATISTICAL METHODS INFECTIOUS METHODS STATISTICAL ROADMAP. Prepared in Support of: CDC/NCEH Cross Sectional Assessment Study. Appendix G STATISTICAL METHODS INFECTIOUS METHODS STATISTICAL ROADMAP Prepared in Support of: CDC/NCEH Cross Sectional Assessment Study Prepared by: Centers for Disease Control and Prevention National

More information

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant

More information

QUALITY MANAGEMENT IN VETERINARY TESTING LABORATORIES

QUALITY MANAGEMENT IN VETERINARY TESTING LABORATORIES CHAPTER 1.1.3. QUALITY MANAGEMENT IN VETERINARY TESTING LABORATORIES SUMMARY Valid laboratory results are essential for diagnosis, surveillance, and trade. Such results are achieved by the use of good

More information

Syllabus. HMI 7437: Data Warehousing and Data/Text Mining for Healthcare

Syllabus. HMI 7437: Data Warehousing and Data/Text Mining for Healthcare Syllabus HMI 7437: Data Warehousing and Data/Text Mining for Healthcare 1. Instructor Illhoi Yoo, Ph.D Office: 404 Clark Hall Email: muteaching@gmail.com Office hours: TBA Classroom: TBA Class hours: TBA

More information