Maintenance of Domain Knowledge for Nursing Care using Data in Hospital Information System
|
|
- Augustine McLaughlin
- 8 years ago
- Views:
Transcription
1 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 of Medicine Shimane University 89-1 Enya-cho Izumo Japan {tsumoto, haruko23, Abstract. Schedule management of hospitalization is important to maintain or improve the quality of medical care and application of a clinical pathway is one of the important solutions for the management. Although several kinds of deductive methods for construction for a clinical pathway have been proposed, the customization is one of the important problems. This research proposed an inductive approach to support the customization of existing clinical pathways by using data on nursing actions stored in a hospital information system. Since hospital data include temporal trends of clinical symptoms and medical services, we can discover not only knowledge about temporal evolution of disease, but also one about medical practice from hospital information system. This paper proposes temporal data mining process and applied the method to capture temporal knowledge about nursing practice. The results show that the reuse of stored data will give a powerful tool for management of nursing schedule and lead to improvement of hospital services. Keywords: temporal data mining; clustering; multidimensional scaling; hospital information system; visualization 1 Introduction Twenty years have passed since clinical data were stored electronically as a hospital information system (HIS)[1 3]. Stored data give all the histories of clinical activities in a hospital, including accounting information, laboratory data and electronic patient records. Due to the traceability of all the information, a hospital cannot function without the information system. All the clinical inputs are shared through the network service in which medical staff can retrieve their information from their terminals [4, 3]. Since all the clinical data are distributed stored and connected as a largescale network, HIS can be viewed as a cyberspace in a hospital: all the results of clinical actions are stored as histories. It is expected that similar techniques in data mining, web mining or network analysis can be applied to the data. Dealing with cyberspace in a hospital will give a new challenging problem in hospital management in which spatiotemporal data mining, social network analysis and DKKD'12 - International Workshop on Domain Knowledge in Knowledge Discovery The 20th International Symposium on Methodologies for Intelligent Systems - ISMIS 2012 Macau, December 4, 2012.
2 2 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto other new data mining methods may play central roles[5, 2]. 1 This paper proposes a temporal data mining method to maintain a clinical pathway used for schedule management of clinincal care. Since the log data of clinical actions and plans are stored in hospital information system, these histories give temporal and procedural information about treatment for each patient. The method consists of the following four steps: first, histories of nursing orders are extracted from hospital information system. Second, orders are classified into several groups by using clustering and multidimensional scaling method. Third, by using the information on groups, feature selection is applied to the data and important features for classification are extracted. Finally, original temporal data are split into several groups and the first step will be repeated. The method was applied to a dataset whose patients had an operation of cataracta. The results show that the reuse of stored data will give a powerful tool for maintenance of clinical pathway, which can be viewed as data-oriented management of nursing schedule. The paper is organized as follows. Section 2 briefly explains clinical pathway, which is schedule management of clinincal process. Section 3 gives explanations on data preparation and mining process. Section 4 gives application of temporal data mining process to analysis of nursing orders, which will lead to revision of clinincal pathway. Finally, Section 5 concludes this paper. 2 Background: Clinical Pathway Since several clinincal actions should be repeated appropriately in the treatment of a disease, schedule management is very important for efficient clinincal process[8, 9]. Such a style of schedule management is called a clinincal pathway. Such each pathway is deductively constructed by doctors or nurses, according to their experiences. For example, Table 1 illustrates a clinincal pathway on cataracta in our university hospital. The whole process of admission will classified into three period: preoperation, operation and post-operation. The preoperation date is denoted by -1 day, and operation date is by 0 day. denotes Body Temporature and Pulse Rate, denotes Blood Pressure. 3 Data Preparation and Analysis 3.1 DWH Since data in hospital information systems are stored as histories of clinical actions, the raw data should be compiled to those accessible to data mining methods. Although this is usually called data-warehousing, medical datawarehousing is different from conventional ones in the following three points. First, since hospital information system consists of distributed and heterogeneous data sources. Second, temporal management is important for medical services, so summarization of data should include temporal information. Third, compilation with several levels of granularity is required. In this paper, we focus on the 1 Application of ordinary statistical methods are shown in [6, 7].
3 Maintenance of Domain Knowledge for Nursing Care 3 Table 1. An Example of Clinincal Pathway Preoperation Operation Postoperation -1day 0day 1day 2day 3day 4day 5day Nausea Nausea Nausea Nausea Nausea Nausea Vomitting Vomitting Vomitting Vomitting Vomitting Vomitting Coaching Coaching Coaching Coaching Coaching Preoperation Instruction Notations. : Body Temprature/Pulse Rate : Blood Pressure number of orders to capture temporal global characteristics of clinical activities, whose scheme is given as Fig 1. Here, data-warehousing has two stages: first, we compile the data from heterogeneous datasets with a given focus as the first DWH. Then, from this DWH, we split the primary DWH into two secondary DWHs: contents DWH and histories DWH. In this analysis, we focus on the latter DWH and we count the number of orders with a given temporal section, compiled into this DWH. Data mining process is applied to the generated data sets from this DWH. Records Prescription Labo Exam Radiology Hospital Information System Temporal Matching Order- History DWH First-DWH Summarization Contents History Second-DWH Data Mining Data Mining Fig. 1. Datawarehousing 3.2 Mining Process Except for the basic process, we will propose temporal data mining process, which consists of the following three steps, shown in Figure 2 We count tem-
4 4 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto poral change of #orders per hour or per days in the second DWH. Then, since each order can be viewed as a temporal sequence, we compare these sequences by calculating similarities. Using similarities, clustering[10], multidimensional scalingmds, and other methods based on similarities are applied. In this paper, all the analysis is conducted by R Second DWH Temporal Datasets Calculation of Similarities (Eucledian Distance, DTW, Multiscale Matching,.) Feature Selection Clustering MDS Correspodence Analysis Decision Tree Induction Rule Induction Statistical Modeling Fig. 2. Mining Process 3.3 Clinincal Pathway Maintenance Process Figure 3 shows the process for maintenance of a clinincal pathway. The right cycle shows the repetition of procedure of temporal data mining process proposed in Fig reffig:process2. The procedure will be terminated when grouping becomes stable and a pathway based on the classification will be constructed. If a clinincal pathway is apriori given, it will be compared with the induced pathway. 4 Construction of Clinincal Pathway Here we apply the above temporal mining process to revision and construction of clinincal pathway Data Preparation We focused on histories of patients who were admitted to the hospital for operations of catracta. The number of patients is 121 in For this disease, the clinincal pathway mentioned in the above section is used to optimize the schedule of treatment. We counted the nursing orders during the stay of each patient and regard chronological change of each order as a temporal sequence. 2 The process follows the method proposed by Tsumoto [11]
5 Maintenance of Domain Knowledge for Nursing Care 5 Datasets Clustering/MDS Labeling Classification of Nursing Orders Yes Grouping Results: Stable? Comparison to Existing Pathway Yes A pathway Exists? No Decision Tree Induction Revision No Construction of Clinical Pathway Feature Selection Fig. 3. Maintenance Process 4.2 First Cycle: Grouping In the first cycle, clustering, MDS and correspondence analysis were applied as the second step in the temporal data mining process. Figure 4 to 6 show the results of clustering, MDS and correspondence analysis of nursing orders with respect to #orders. Clustering results gives two major groups: one includes the orders indispensable to this disease and the other includes those which are rather specific to the status of each patient, except for preoperation instruction. MDS gives further classification of the first group into three subgroups and the second one into two subgroups The former three subgroups consist of vital signs (, BT and PR), body care (Coaching and ), and watchlist (Eye Symptoms and Nausea). The latter two groups consist of preoperation instruction and other symptoms which may specific to the status to the patients. On the other hand, correspondence analysis did not give good information about correspondence between length of stay and nursing orders. This may be because the length of stay was too short (within a week) and all the nursing orders were well scheduled due to the application of clinical pathway. The results also give another interesting observation: comparison of the above results with the existing clinical pathway shows that the pathway lacks two orders, and Coach, although their temporal patterns are very similar to other orders indispensable to the treatment. So, they should be included into clinical pathway. In this way, these method can be used to evaluate the existing pathway for a disease and revise it. 4.3 First Cycle: Feature Selection With the labels obtained, decision tree induction[12] was applied to the dataset. Figure 7 shows the result where only selected attribute is d1, the first day of
6 6 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto Height dist_iwata_opt3 hclust (*, "ward") Preoperation Instruction Shampoo BS(Ward) Nerve Others Bath SPO2 Skin/Nail (Ns)Vital Sign Vital Sign (Ns) DM management Fall Prevention Fall Prevention (Bed) (Ns) Guidance for Disease Eye Symptoms Ward s method Fig. 4. Clustering Results of Nursing Orders (Cataracta) iwata_opt_mds3[,2] Preoperation Instruction (Ns)Vital Sign Sign all Prevention BS(Ward) Guidance SPO2 Others Nerve (Ns) (Bed) Skin/Nail DM Bath management (Ns) for Disease Shampoo Eye Symptoms iwata_opt_mds3[,1] Fig. 5. MDS Results of Nursing Orders (Cataracta) postoperation period. In the induced tree, d1 < 23 to the right means that if the number of a given order is less than 23, the order belongs to the right class (which is specific to the status of patients.) In other words, Table 2 shows the list of information gain for the labels. These values show that for d1, d2 and d3 give complete classification for the labels, and preoperation, postoperation, d4 and d5 do not have enough information for complete classification. Thus, the data should be split into three groups: the first interval is
7 Maintenance of Domain Knowledge for Nursing Care Preoperation ( 1) ation Instruction BS(Ward) Operation (0) Vital Sign (Ns)Vital Sign Nerve SPO2 Fall Prevention Fall Prevention (Bed) 2 DM (Ns) management Others 1 3 Guidance (Ns) Eye Shampoo Symptoms for Disease Bath Skin/Nail Fig. 6. Results of Correspondence Analysis of Nursing Orders (Cataracta) d1>=23 c2 7/18 c1 7/0 c2 0/18 Fig. 7. Decision Tree (Cataracta) preoperation and operation, the second one is d1 to d3, and the final interval is d4 and d5. In the next cycle, the same procedures will applied to each subset of data. 4.4 Second Cycle: Grouping Figure 8 and 9 show the results for preoperation and operation (-1 to 0 days), which gives much clearer results. Here we can see three clusters, compared with
8 8 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto Table 2. Information Gain for Each Attribute Node Information Gain preoperation < 8 to the right operation < 23 to the right d1 < 23 to the right d2 < 21 to the right d3 < 39 to the right d4 < 27.5 to the right 6.48 d5 < 18.5 to the right 6.48 Height dist_iwata_opt4a hclust (*, "ward") Bath Skin/Nail SPO2 Shampoo BS(Ward) Guidance for Disease Others Nerve (Ns)Vital Sign Vital Sign (Ns) DM management (Ns) Fall Prevention Fall Prevention (Bed) Eye Symptoms Preoperation Instruction Ward s method Fig. 8. Clustering Results of Nursing Orders (Cataracta: -1 to 0 days) the former results. Although Coaching and belongs to the same cluster in the first cycle, the former is close to Eye symptoms, Nausea/Vomiting and, the latter is close to, and Preoperation instruction. Since the former three observations are mainly for postoperation follow-up, Coaching may belong to the postoperation follow-up process. However, may belong to the whole process. Figure 9 supports this obeservation. Next, Figure 10 and 11 show the results for 1 to 3 days. Here the results are very similar to those obtained from the whole data. However, Coaching is similar to, and is close to and, and Eye symptoms. Finally, Figure 12 and 13 show the results for 4 and 5 days. Here the grouping is slightly different from the second one and Coaching and are not similar to,,, and Eye symptoms.
9 Maintenance of Domain Knowledge for Nursing Care 9 iwata_opt_mds4a[,2] Guidance (Ns) all Prevention DM Shampoo Skin/Nail BS(Ward) SPO2 Others Nerve Bath (Ns) management for Disease (Ns)Vital (Bed) Sign Vital Sign Eye Symptoms Preoperation Instruction iwata_opt_mds4a[,1] Fig. 9. MDS Results of Nursing Orders (Cataracta: -1 to 0 days) Height dist_iwata_opt4b2 hclust (*, "ward") Shampoo Others Nerve SPO2 BS(Ward) Bath Skin/Nail Preoperation Instruction (Ns) (Ns) DM management Fall Prevention (Bed) Guidance for Disease Fall Prevention (Ns)Vital Sign Vital Sign Eye Symptoms Ward s method Fig. 10. Clustering Results of Nursing Orders (Cataracta: 1 to 3 days) In either case, the labels were provided by the grouping methods for each period, and decision tree induction was applied. Each attirbute gave complete classification, so further data partition was not needed, and the repetition of grouping and feature selection was terminated.
10 10 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto iwata_opt_mds4b2[,2] (Ns)Vital Sign Sign Guidance BS(Ward) SPO2 Others for Disease Shampoo Preoperation all Prevention all Prevention Skin/Nail DM Nerve Instruction (Bed) Bath management (Ns) (Ns) Eye Symptoms iwata_opt_mds4b2[,1] Fig. 11. MDS Results of Nursing Orders (Cataracta: 1 to 3 days) Height dist_iwata_opt4c hclust (*, "ward") Others Guidance for Disease Skin/Nail Vital Sign Bath Fall Prevention (Ns)Vital Sign Nerve Preoperation Instruction BS(Ward) SPO2 (Ns) Fall Prevention (Bed) Shampoo (Ns) DM management Eye Symptoms Ward s method Fig. 12. Clustering Results of Nursing Orders (Cataracta: 4 to 5 days) 4.5 Construction of Pathway From those results, coaching and wash, whose chronological characteristics are similar to the orders indispensable to the treatment of cataracta, were not included in the existing pathway, but should be added to the pathway. Furthermore, coaching and wash should be treated as postoperation follow-up and routine process, respectively. Thus, the pathway was revised as shown in Table 3.
11 Maintenance of Domain Knowledge for Nursing Care 11 Others Skin/Nail Guidance Vital Sign for Disease Preoperation BS(Ward) DM SPO2 management Instruction iwata_opt_mds4c[,2] (Ns)Vital all Prevention Nerve BathSign (Ns) all Prevention (Bed) (Ns) Shampoo Eye Symptoms iwata_opt_mds4c[,1] Fig. 13. MDS Results of Nursing Orders (Cataracta: 4 to 5 days) In summary, temporal data mining of nursing may be useful for to construct clinical pathway for a disease where a pathway is not introduced and to revise the existing pathway. 5 Conclusions In this paper, we propose a general framework on innovation of hospital services based on temporal data mining process. This process can be called similaritybased visualization approach in which similarity-based methods, such as clustering and multidimensional scaling (MDS) and correspondence analysis. We applied the process to datasets of #nursing orders for cases for operation of cataracta where clinincal pathway has been introduced. By using Clustering and MDS, we obtained two major groups in the nursing orders: ones were indispensable to the treatment, and the others were specific to the status of patients. Then, in the step for feature selection, the first day of postoperation could be be viewed as a threshold in the original datasets. Thus, periods before and after operation should be dealt as independent datasets. Repeating these steps, we could characterize the temporal aspects of nursing orders, and then found missing information in the existing pathway. This paper is a preliminary approach to data-mining hospital management towards a innovative process for hospital services. More detailed analysis will be reported in the near future. Acknowledgments This research is supported by Grant-in-Aid for Scientific Research (B) from Japan Society for the Promotion of Science(JSPS).
12 12 Haruko Iwata, Shoji Hirano and Shusaku Tsumoto Table 3. Revised Clinincal Pathway for Cataracta Preoperation Operation Postoperation -1day 0day 1day 2day 3day 4day 5day Nausea Nausea Nausea Nausea Nausea Nausea Vomitting Vomitting Vomitting Vomitting Vomitting Vomitting Eye Symp. Eye Symp. Eye Symp. Eye Symp. Eye Symp. Coaching Coaching Coaching Preoperation Instruction Notations. : Body Temprature/Pulse Rate. : Blood Pressure. Eye Symp: Eye Symptoms. References 1. Shortliffe, E., Cimino, J., eds.: Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 3rd edn. Springer (2006) 2. Bichindaritz, I.: Memoire: A framework for semantic interoperability of case-based reasoning systems in biology and medicine. Artif Intell Med 36(2) (2006) Tsumoto, S., Hirano, S.: Risk mining in medicine: Application of data mining to medical risk management. Fundam. Inform. 98(1) (2010) Hanada, E., Tsumoto, S., Kobayashi, S.: A hubiquitous environmenth through wireless voice/data communication and a fully computerized hospital information system in a university hospital. In Takeda, H., ed.: E-Health. Volume 335 of IFIP Advances in Information and Communication Technology. Springer Boston (2010) Iakovidis, D., Smailis, C.: A semantic model for multimodal data mining in healthcare information systems. Stud Health Technol Inform 180 (2012) Tsumoto, Y., Tsumoto, S.: Exploratory univariate analysis on the characterization of a university hospital: A preliminary step to data-mining-based hospital management using an exploratory univariate analysis of a university hospital. The Review of Socionetwork Strategies 4(2) (2010) Tsumoto, Y., Tsumoto, S.: Correlation and regression analysis for characterization of university hospital (submitted). The Review of Socionetwork Strategies 5(2) (2011) Hyde, E., Murphy, B.: Computerized clinical pathways (care plans): piloting a strategy to enhance quality patient care. Clin Nurse Spec 26(4) (2012) Ward, M., Vartak, S., Schwichtenberg, T., Wakefield, D.: Nurses perceptions of how clinical information system implementation affects workflow and patient care. Comput Inform Nurs 29(9) (2011) Everitt, B.S., Landau, S., Leese, M., Stahl, D.: Cluster Analysis. 5th edn. Wiley (2011)
13 Maintenance of Domain Knowledge for Nursing Care Tsumoto, S., Hirano, S., Tsumoto, Y.: Clustering-based analysis in hospital information systems. In: Proceedings of GrC2011, IEEE Computer Society (2010) 12. Quinlan, J.: Induction of decision trees. Machine Learning 1 (1986)
Temporal Data Mining in Hospital Information Systems: Analysis of Clinical Courses of Chronic Hepatitis
Vol. 1, No. 1, Issue 1, Page 11 of 19 Copyright 2007, TSI Press Printed in the USA. All rights reserved Temporal Data Mining in Hospital Information Systems: Analysis of Clinical Courses of Chronic Hepatitis
More informationBehavior Grouping based on Trajectories Mining. Department of Medical Informatics Shimane University, School of Medicine, Japan
Behavior Grouping based on Trajectories Mining Shoji Hirano Shusaku Tsumoto Department of Medical Informatics Shimane University, School of Medicine, Japan 1 Introduction Outline Background, Objective,
More informationData Mining for Risk Management in Hospital Information Systems
Data Mining for Risk Management in Hospital Information Systems Shusaku Tsumoto and Shoji Hirano Department of Medical Informatics, Shimane University, School of Medicine, 89-1 Enya-cho, Izumo 693-8501
More informationEvaluating an Integrated Time-Series Data Mining Environment - A Case Study on a Chronic Hepatitis Data Mining -
Evaluating an Integrated Time-Series Data Mining Environment - A Case Study on a Chronic Hepatitis Data Mining - Hidenao Abe, Miho Ohsaki, Hideto Yokoi, and Takahira Yamaguchi Department of Medical Informatics,
More informationInternational 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 informationData Quality Mining: Employing Classifiers for Assuring consistent Datasets
Data Quality Mining: Employing Classifiers for Assuring consistent Datasets Fabian Grüning Carl von Ossietzky Universität Oldenburg, Germany, fabian.gruening@informatik.uni-oldenburg.de Abstract: Independent
More informationSPATIAL DATA CLASSIFICATION AND DATA MINING
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal
More informationA 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 informationInformation Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)
More informationA Semantic Model for Multimodal Data Mining in Healthcare Information Systems
A Semantic Model for Multimodal Data Mining in Healthcare Information Systems Dimitris IAKOVIDIS 1 and Christos SMAILIS Department of Informatics and Computer Technology, Technological Educational Institute
More informationBiomedical Informatics: Computer Applications in Health Care and Biomedicine
An Overview of Biomedical Date 1/19/06 Biomedical : Computer Applications in Health Care and Biomedicine Edward H. Shortliffe, MD, PhD Department of Biomedical Columbia University Asian Pacific Association
More informationData Mining Solutions for the Business Environment
Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over
More informationDATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 DATA MINING TECHNIQUES SUPPORT TO KNOWLEGDE OF BUSINESS INTELLIGENT SYSTEM M. Mayilvaganan 1, S. Aparna 2 1 Associate
More informationDatabase Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski
More informationExploration and Visualization of Post-Market Data
Exploration and Visualization of Post-Market Data Jianying Hu, PhD Joint work with David Gotz, Shahram Ebadollahi, Jimeng Sun, Fei Wang, Marianthi Markatou Healthcare Analytics Research IBM T.J. Watson
More informationA 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 informationBOOSTING - 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 informationPractical Implementation of a Bridge between Legacy EHR System and a Clinical Research Environment
Cross-Border Challenges in Informatics with a Focus on Disease Surveillance and Utilising Big-Data L. Stoicu-Tivadar et al. (Eds.) 2014 The authors. This article is published online with Open Access by
More informationTOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM
TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam
More informationDivide-n-Discover Discretization based Data Exploration Framework for Healthcare Analytics
for Healthcare Analytics Si-Chi Chin,KiyanaZolfaghar,SenjutiBasuRoy,AnkurTeredesai,andPaulAmoroso Institute of Technology, The University of Washington -Tacoma,900CommerceStreet,Tacoma,WA980-00,U.S.A.
More informationINTERNATIONAL 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 informationInternational Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET
DATA MINING TECHNIQUES AND STOCK MARKET Mr. Rahul Thakkar, Lecturer and HOD, Naran Lala College of Professional & Applied Sciences, Navsari ABSTRACT Without trading in a stock market we can t understand
More informationBig Data with Rough Set Using Map- Reduce
Big Data with Rough Set Using Map- Reduce Mr.G.Lenin 1, Mr. A. Raj Ganesh 2, Mr. S. Vanarasan 3 Assistant Professor, Department of CSE, Podhigai College of Engineering & Technology, Tirupattur, Tamilnadu,
More informationAn Overview of Knowledge Discovery Database and Data mining Techniques
An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,
More informationIntroduction. 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 informationAssociation Technique on Prediction of Chronic Diseases Using Apriori Algorithm
Association Technique on Prediction of Chronic Diseases Using Apriori Algorithm R.Karthiyayini 1, J.Jayaprakash 2 Assistant Professor, Department of Computer Applications, Anna University (BIT Campus),
More informationMeta-learning. Synonyms. Definition. Characteristics
Meta-learning Włodzisław Duch, Department of Informatics, Nicolaus Copernicus University, Poland, School of Computer Engineering, Nanyang Technological University, Singapore wduch@is.umk.pl (or search
More informationData Mining and Machine Learning in Bioinformatics
Data Mining and Machine Learning in Bioinformatics PRINCIPAL METHODS AND SUCCESSFUL APPLICATIONS Ruben Armañanzas http://mason.gmu.edu/~rarmanan Adapted from Iñaki Inza slides http://www.sc.ehu.es/isg
More informationInteractive Information Visualization of Trend Information
Interactive Information Visualization of Trend Information Yasufumi Takama Takashi Yamada Tokyo Metropolitan University 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan ytakama@sd.tmu.ac.jp Abstract This paper
More informationMining 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 informationIntroduction to Data Mining
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
More informationMED 2400 MEDICAL INFORMATICS FUNDAMENTALS
MED 2400 MEDICAL INFORMATICS FUNDAMENTALS NEW YORK CITY COLLEGE OF TECHNOLOGY The City University Of New York School of Arts and Sciences Biological Sciences Department Course title: Course code: MED 2400
More informationPREDICTIVE ANALYTICS: PROVIDING NOVEL APPROACHES TO ENHANCE OUTCOMES RESEARCH LEVERAGING BIG AND COMPLEX DATA
PREDICTIVE ANALYTICS: PROVIDING NOVEL APPROACHES TO ENHANCE OUTCOMES RESEARCH LEVERAGING BIG AND COMPLEX DATA IMS Symposium at ISPOR at Montreal June 2 nd, 2014 Agenda Topic Presenter Time Introduction:
More informationBetter Healthcare with Data Mining
Technical report Better Healthcare with Data Mining Philip Baylis Shared Medical Systems Limited, UK Table of contents Abstract... 2 Introduction... 2 Inpatient length of stay... 2 Patient data... 3 Detect
More informationISSUES IN RULE BASED KNOWLEDGE DISCOVERING PROCESS
Advances and Applications in Statistical Sciences Proceedings of The IV Meeting on Dynamics of Social and Economic Systems Volume 2, Issue 2, 2010, Pages 303-314 2010 Mili Publications ISSUES IN RULE BASED
More informationHealthcare Data Mining: Prediction Inpatient Length of Stay
3rd International IEEE Conference Intelligent Systems, September 2006 Healthcare Data Mining: Prediction Inpatient Length of Peng Liu, Lei Lei, Junjie Yin, Wei Zhang, Wu Naijun, Elia El-Darzi 1 Abstract
More informationPattern Mining and Querying in Business Intelligence
Pattern Mining and Querying in Business Intelligence Nittaya Kerdprasop, Fonthip Koongaew, Phaichayon Kongchai, Kittisak Kerdprasop Data Engineering Research Unit, School of Computer Engineering, Suranaree
More informationIMPROVING DATA INTEGRATION FOR DATA WAREHOUSE: A DATA MINING APPROACH
IMPROVING DATA INTEGRATION FOR DATA WAREHOUSE: A DATA MINING APPROACH Kalinka Mihaylova Kaloyanova St. Kliment Ohridski University of Sofia, Faculty of Mathematics and Informatics Sofia 1164, Bulgaria
More informationProcess Modelling from Insurance Event Log
Process Modelling from Insurance Event Log P.V. Kumaraguru Research scholar, Dr.M.G.R Educational and Research Institute University Chennai- 600 095 India Dr. S.P. Rajagopalan Professor Emeritus, Dr. M.G.R
More informationUsing the Grid for the interactive workflow management in biomedicine. Andrea Schenone BIOLAB DIST University of Genova
Using the Grid for the interactive workflow management in biomedicine Andrea Schenone BIOLAB DIST University of Genova overview background requirements solution case study results background A multilevel
More informationDistributed Database for Environmental Data Integration
Distributed Database for Environmental Data Integration A. Amato', V. Di Lecce2, and V. Piuri 3 II Engineering Faculty of Politecnico di Bari - Italy 2 DIASS, Politecnico di Bari, Italy 3Dept Information
More informationIntroduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing
Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition
More informationData Mining On Diabetics
Data Mining On Diabetics Janani Sankari.M 1,Saravana priya.m 2 Assistant Professor 1,2 Department of Information Technology 1,Computer Engineering 2 Jeppiaar Engineering College,Chennai 1, D.Y.Patil College
More informationMedical Big Data Interpretation
Medical Big Data Interpretation Vice president of the Xiangya Hospital, Central South University The director of the ministry of mobile medical education key laboratory Professor Jianzhong Hu BIG DATA
More informationChapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
More informationClustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca
Clustering Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?
More informationA Data Grid Model for Combining Teleradiology and PACS Operations
MEDICAL IMAGING TECHNOLOGY Vol.25 No.1 January 2007 7 特 集 論 文 / 遠 隔 医 療 と 画 像 通 信 A Data Grid Model for Combining Teleradiology and Operations H.K. HUANG *, Brent J. LIU *, Zheng ZHOU *, Jorge DOCUMET
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015
RESEARCH ARTICLE OPEN ACCESS Data Mining Technology for Efficient Network Security Management Ankit Naik [1], S.W. Ahmad [2] Student [1], Assistant Professor [2] Department of Computer Science and Engineering
More informationData Mining and Analysis of Online Social Networks
Data Mining and Analysis of Online Social Networks R.Sathya 1, A.Aruna devi 2, S.Divya 2 Assistant Professor 1, M.Tech I Year 2, Department of Information Technology, Ganadipathy Tulsi s Jain Engineering
More informationVolume 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 informationA Review of Data Mining Techniques
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. 3, Issue. 4, April 2014,
More informationThree types of messages: A, B, C. Assume A is the oldest type, and C is the most recent type.
Chronological Sampling for Email Filtering Ching-Lung Fu 2, Daniel Silver 1, and James Blustein 2 1 Acadia University, Wolfville, Nova Scotia, Canada 2 Dalhousie University, Halifax, Nova Scotia, Canada
More informationClinic + - A Clinical Decision Support System Using Association Rule Mining
Clinic + - A Clinical Decision Support System Using Association Rule Mining Sangeetha Santhosh, Mercelin Francis M.Tech Student, Dept. of CSE., Marian Engineering College, Kerala University, Trivandrum,
More informationInvestigating Clinical Care Pathways Correlated with Outcomes
Investigating Clinical Care Pathways Correlated with Outcomes Geetika T. Lakshmanan, Szabolcs Rozsnyai, Fei Wang IBM T. J. Watson Research Center, NY, USA August 2013 Outline Care Pathways Typical Challenges
More informationProcess mining challenges in hospital information systems
Proceedings of the Federated Conference on Computer Science and Information Systems pp. 1135 1140 ISBN 978-83-60810-51-4 Process mining challenges in hospital information systems Payam Homayounfar Wrocław
More informationA Semantic Model for Multimodal Data Mining in Healthcare Information Systems. D.K. Iakovidis & C. Smailis
A Semantic Model for Multimodal Data Mining in Healthcare Information Systems D.K. Iakovidis & C. Smailis Department of Informatics and Computer Technology Technological Educational Institute of Lamia,
More informationPreface: Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Fundamenta Informaticae 90 (2009) i vii DOI 10.3233/FI-2009-0001 IOS Press i Preface: Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II) Yingxu Wang Visiting
More informationMedical Informatics An Overview Saudi Board For Community Medicine
Medical Informatics An Overview Saudi Board For Community Medicine Ahmed AlBarrak PhD Medical Informatics Associate Professor of Health Informatics, Family & Community Med, Chairman, Medical Informatics,
More informationISSN: 2320-1363 CONTEXTUAL ADVERTISEMENT MINING BASED ON BIG DATA ANALYTICS
CONTEXTUAL ADVERTISEMENT MINING BASED ON BIG DATA ANALYTICS A.Divya *1, A.M.Saravanan *2, I. Anette Regina *3 MPhil, Research Scholar, Muthurangam Govt. Arts College, Vellore, Tamilnadu, India Assistant
More informationUsing Ontologies in Proteus for Modeling Data Mining Analysis of Proteomics Experiments
Using Ontologies in Proteus for Modeling Data Mining Analysis of Proteomics Experiments Mario Cannataro, Pietro Hiram Guzzi, Tommaso Mazza, and Pierangelo Veltri University Magna Græcia of Catanzaro, 88100
More informationStandardization and Its Effects on K-Means Clustering Algorithm
Research Journal of Applied Sciences, Engineering and Technology 6(7): 399-3303, 03 ISSN: 040-7459; e-issn: 040-7467 Maxwell Scientific Organization, 03 Submitted: January 3, 03 Accepted: February 5, 03
More informationApplication of Data Mining Methods in Health Care Databases
6 th International Conference on Applied Informatics Eger, Hungary, January 27 31, 2004. Application of Data Mining Methods in Health Care Databases Ágnes Vathy-Fogarassy Department of Mathematics and
More informationA NURSING CARE PLAN RECOMMENDER SYSTEM USING A DATA MINING APPROACH
Proceedings of the 3 rd INFORMS Workshop on Data Mining and Health Informatics (DM-HI 8) J. Li, D. Aleman, R. Sikora, eds. A NURSING CARE PLAN RECOMMENDER SYSTEM USING A DATA MINING APPROACH Lian Duan
More informationAn Analysis of Missing Data Treatment Methods and Their Application to Health Care Dataset
P P P Health An Analysis of Missing Data Treatment Methods and Their Application to Health Care Dataset Peng Liu 1, Elia El-Darzi 2, Lei Lei 1, Christos Vasilakis 2, Panagiotis Chountas 2, and Wei Huang
More informationPractical Applications of DATA MINING. Sang C Suh Texas A&M University Commerce JONES & BARTLETT LEARNING
Practical Applications of DATA MINING Sang C Suh Texas A&M University Commerce r 3 JONES & BARTLETT LEARNING Contents Preface xi Foreword by Murat M.Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction
More informationMEDICAL/CERTIFIED MEDICAL ASSISTANT
MEDICAL/CERTIFIED MEDICAL ASSISTANT Occ. Work Prob. Effective Last Code No. Class Title Area Area Period Date Action 4547 Medical Assistant 12 442 6 mo. 07/15/12 Rev. 0000 Certified Medical Assistant 12
More informationBusiness Intelligence Using Data Mining Techniques on Very Large Datasets
International Journal of Science and Research (IJSR) Business Intelligence Using Data Mining Techniques on Very Large Datasets Arti J. Ugale 1, P. S. Mohod 2 1 Department of Computer Science and Engineering,
More informationTHE 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 information131-1. 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 informationPharmaSUG2011 Paper HS03
PharmaSUG2011 Paper HS03 Using SAS Predictive Modeling to Investigate the Asthma s Patient Future Hospitalization Risk Yehia H. Khalil, University of Louisville, Louisville, KY, US ABSTRACT The focus of
More informationBig Data Mining Services and Knowledge Discovery Applications on Clouds
Big Data Mining Services and Knowledge Discovery Applications on Clouds Domenico Talia DIMES, Università della Calabria & DtoK Lab Italy talia@dimes.unical.it Data Availability or Data Deluge? Some decades
More informationModeling 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 informationMEDICAL SCIENCES Vol.II -Human Aspects of Health Care Information Systems - Andre Kushniruk, Joseph Kannry
HUMAN ASPECTS OF HEALTH CARE INFORMATION SYSTEMS Andre Kushniruk School of Health Information Science,University of Victoria, Victoria, British Columbia, Canada Joseph Kannry Mt. Sinai Medical Center,
More informationThe American Academy of Ophthalmology Adopts SNOMED CT as its Official Clinical Terminology
The American Academy of Ophthalmology Adopts SNOMED CT as its Official Clinical Terminology H. Dunbar Hoskins, Jr., M.D., P. Lloyd Hildebrand, M.D., Flora Lum, M.D. The road towards broad adoption of electronic
More informationChapter 20: Data Analysis
Chapter 20: Data Analysis Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 20: Data Analysis Decision Support Systems Data Warehousing Data Mining Classification
More informationSyllabus. 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 informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationData Mining Analysis of a Complex Multistage Polymer Process
Data Mining Analysis of a Complex Multistage Polymer Process Rolf Burghaus, Daniel Leineweber, Jörg Lippert 1 Problem Statement Especially in the highly competitive commodities market, the chemical process
More informationAvailable online at www.sciencedirect.com Available online at www.sciencedirect.com. Advanced in Control Engineering and Information Science
Available online at www.sciencedirect.com Available online at www.sciencedirect.com Procedia Procedia Engineering Engineering 00 (2011) 15 (2011) 000 000 1822 1826 Procedia Engineering www.elsevier.com/locate/procedia
More informationMining of predictive patterns in Electronic health records data
Mining of predictive patterns in Electronic health records data Iyad Batal and Milos Hauskrecht Department of Computer Science University of Pittsburgh milos@cs.pitt.edu 1 Introduction The emergence of
More informationLearning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal
Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether
More informationProcess Mining. ^J Springer. Discovery, Conformance and Enhancement of Business Processes. Wil M.R van der Aalst Q UNIVERS1TAT.
Wil M.R van der Aalst Process Mining Discovery, Conformance and Enhancement of Business Processes Q UNIVERS1TAT m LIECHTENSTEIN Bibliothek ^J Springer Contents 1 Introduction I 1.1 Data Explosion I 1.2
More informationCluster analysis and Association analysis for the same data
Cluster analysis and Association analysis for the same data Huaiguo Fu Telecommunications Software & Systems Group Waterford Institute of Technology Waterford, Ireland hfu@tssg.org Abstract: Both cluster
More informationSentiment analysis on tweets in a financial domain
Sentiment analysis on tweets in a financial domain Jasmina Smailović 1,2, Miha Grčar 1, Martin Žnidaršič 1 1 Dept of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia 2 Jožef Stefan International
More informationData Mining. 1 Introduction 2 Data Mining methods. Alfred Holl Data Mining 1
Data Mining 1 Introduction 2 Data Mining methods Alfred Holl Data Mining 1 1 Introduction 1.1 Motivation 1.2 Goals and problems 1.3 Definitions 1.4 Roots 1.5 Data Mining process 1.6 Epistemological constraints
More informationCollaboration with Patients for Prevention of Medical Accidents Caused by Patient Action
Collaboration with Patients for Prevention of Medical Accidents Caused by Patient Action Shogo Kato 1*, Fumio Fukumura 2, Satoko Tsuru 3, and Yoshinori Iizuka 4 1 The University of Tokyo, kato@tqm.t.u-tokyo.ac.jp
More informationData Mining Applications in Higher Education
Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2
More informationTracking Groups of Pedestrians in Video Sequences
Tracking Groups of Pedestrians in Video Sequences Jorge S. Marques Pedro M. Jorge Arnaldo J. Abrantes J. M. Lemos IST / ISR ISEL / IST ISEL INESC-ID / IST Lisbon, Portugal Lisbon, Portugal Lisbon, Portugal
More informationA 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 informationPrediction 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 informationMachine Learning and Data Mining. Fundamentals, robotics, recognition
Machine Learning and Data Mining Fundamentals, robotics, recognition Machine Learning, Data Mining, Knowledge Discovery in Data Bases Their mutual relations Data Mining, Knowledge Discovery in Databases,
More informationData quality in Accounting Information Systems
Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania
More informationKeywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.
Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics
More informationB. 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 informationInternational Journal of Innovative Research in Computer and Communication Engineering
FP Tree Algorithm and Approaches in Big Data T.Rathika 1, J.Senthil Murugan 2 Assistant Professor, Department of CSE, SRM University, Ramapuram Campus, Chennai, Tamil Nadu,India 1 Assistant Professor,
More informationTransformation of Free-text Electronic Health Records for Efficient Information Retrieval and Support of Knowledge Discovery
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
More informationPredicting 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 informationE-Learning Using Data Mining. Shimaa Abd Elkader Abd Elaal
E-Learning Using Data Mining Shimaa Abd Elkader Abd Elaal -10- E-learning using data mining Shimaa Abd Elkader Abd Elaal Abstract Educational Data Mining (EDM) is the process of converting raw data from
More informationIV Semester M.B.A. (Part Time) Degree Examination, July 2009 (2006 Scheme) MANAGEMENT INFORMATION SYSTEM
*3726* (Pages : 7) 3726 Reg. No. :... Name :... IV Semester M.B.A. (Part Time) Degree Examination, July 2009 (2006 Scheme) MANAGEMENT INFORMATION SYSTEM Time : 3 Hours Max. Marks : 60 PART A Write short
More informationQuality Control of National Genetic Evaluation Results Using Data-Mining Techniques; A Progress Report
Quality Control of National Genetic Evaluation Results Using Data-Mining Techniques; A Progress Report G. Banos 1, P.A. Mitkas 2, Z. Abas 3, A.L. Symeonidis 2, G. Milis 2 and U. Emanuelson 4 1 Faculty
More information