پایان نامه با کلید واژه های Technology، بیمارستان، دانشگاه فردوسی

Communication. 2005;3(4):19-28.
28 Hu D. Study on information system of health care services management in hospital. Services Systems and Services Management, 2005 Proceedings of ICSSSM ’05 2005 International Conference on 13-15 June 2005: IEEE; 2005. p. 1498 – 501.
.29 م. آقاجانی، بررسی و مقایسه سیستم های اطلاعات بیمارستانی. طب وتزکیه 1382; دوره 47
30. Khan NN. Hospital Information Systems: An Aid to Decision Making. Emerging Trends in Engineering and Technology (ICETET), 2010 3rd International Conference on; 19-21 Nov. 2010; Goa: IEEE; 2010. p. 657 – 63.
31. Xiaolan W. Improved Services in Hospital Information System. Information Technology and Applications (IFITA), 2010 International Forum on 16-18 July 2010; Kunming: IEEE; 2010. p. 358-61.
32. Ahmadi M. A Survey of Usability of Hospital Information Systems from the perspective of Nurses, Department Secretaries, and paraclinic Users in Selected hospitals: 2009 Journal of Health Administration. 2011;14(44):11-20.
33. Lenz R. Intranet meets hospital information systems – the solution to the integration.problem? Method Inform Med. 2001;40:99-105.
34. Collen M. A brief historical overview of hospital information system evolution in the United States. Int JBiomed Comput. 1991;29(3-4):169-89.
.35 م. قاضی سعیدی، مديريت اطلاعات بهداشتي درمانی، ماهان – تهران 1384.
.36 د.ترابی، مدیریت فناوری اطلاعات سلامت ،انتشارات جعفري 1389.
.37 د.احمدی، مديريت اطلاعات بهداشتي: مديريت يك منبع استراتژيك،انتشارات واژه پرداز 1382.
38. Fayyad U. From data mining to knowledge discovery in databases. AI Mag. 1996;17(3):37-54.
39. Friedman J. Data Mining and Statistics: What’s the connection? Comput Sci Stat. 1998.
40. ع.مشکانی، مقدمه ای برداده کاوی، موسسه چاپ وانتشارات دانشگاه فردوسی مشهد،1388 .
41. Potomac. Two Crows Corporation, Introduction to Data Mining and Knowledge Discovery. Third ed: Two Crows Corporation; 1999.
42. Gupta S. Data Mining Classification Techniques Applied For Breast Cancer Diagnosis And Prognosis Indian Journal of Computer Science and Engineering (IJCSE). 2011:188-95.
43. Bushinak H. Recognizing The Electronic Medical Record Data FromUnstructured Medical Data Using Visual Text Mining TechniquesProf. Hussain Bushinak. InternationalJournal of Computer Science and Information Security. 2011;9(6):25-35.
44. Seifert JW. Data Mining : An Overview. Analyst in Information Science and Technology Policy, Resources S, and Industry Division; 2004.
45. Stühlinger W. Intelligent Data Mining for Medical Qualit 2000.
46. Ganesan N. .Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data. International Journal of Computer Applications 2010;1(26):0975-8887.
47. Haykin S. ; “Neural Networks: A Comprehensive Foundation second ed: Prentice-Hall Inc; 1999.
48. م. منهاج. مبانی شبکه های عصبی، دانشگاه صنعتی امیرکبیر، تهران 1392.
49. Machová K. A Bagging Method using Decision Trees in the Role of Base Classifiers Acta Polytechnica Hungarica. 2006;3(2).
50. Dietterich TG. An experimental comparison of three methods for constructing ensembles of decision trees:Bagging, boosting, and randomization Machine Learning. 2000;40(2):139-58.
51. Freund Y. Boosting a weak learning algorithm by majority. Information and Computation. 1995;121(2):256-85.
52. Skurichina M. The Role of Combining Rules in Bagging and Boosting 2004.
53. Demiriz A. Linear programming boosting via column generation. Machine Learning. 2002;46:225-54.
54. Hao X. An Improved Adaboost.R Algorithm and Its Application in Mining Safety Monitoring. Intelligent Information Technology Application, 2009 IITA 2009 Third International Symposium on; 21-22 Nov. 2009; Nanchang: IEEE; 2009. p. 287-90.
55. Solomatine DP. AdaBoost.RT: a Boosting Algorithm for Regression Problems. IEEE; 2004. p. 7803-8359.
56. Basak D. Support vector regression. Neural Inf Process. 2007;11:203-25.
57. Guohai L. Model optimization of SVM for a fermentation soft sensor with Applications2010.
58. Liu Y. Soft chemical analyzer development using adaptive least-squares support .vector regression with selective pruning and variable moving window size”, ., Vol. 48, pp.5731–574, 2009. Ind Eng Chem Res. 2009;48:5731-40.
59. Hong WC. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing. 2011;74:2096-107.
60. Yin J. LogP prediction for blocked tripeptides with amino acids descriptors (HMLP) by multiple linear regressionand support vector regression. Procedia Environmental Sciences 2011;8:173-8.
61. Vapnik VN. The nature of statistical learning theory. second ed. New York: Springer; 1999.
62. Boser BE. A training algorithm for optimal margin classifiers. In: Haussler D, editor. 5th Annual ACM Workshop on COLT; Pittsburgh: ACM Press; 1992. p. 144-52.
63. L. Xu. Comparisons of Logistic Regression and Artificial Neural Network on Power DistributionSystems Fault Cause Identification. Mid-Summer Workshop on Soft Computing in Industrial Applications; June ٢٨-٣٠; Finland: IEEE; 2005.
64 Kim YS. Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Systems with Applications: Elsevier; 2008. p. 1227-34.
65. “ Data Mining Software Suites”. [Online]. Available : http://www.kdnuggets.com /software/suites.html.
66. Lavrac N. Selected techniques for data mining in medicine, 16 (1999) 3–23. Artificial Intelligence in Medicine. 1999;3:16-23.
67. ” بیمارستان پاستور بم ” [Online]. Available: http://www.mubam.ac.ir
68. “شرکت تیراژه رایانه”[Online]. Available: http://www.trtco.com
69. Smola AJ. A tutorial on support vector regression: Springer 2004.
70. Bertoni A. A Boosting Algorithm for Regression. Available from: http://www.researchgate.net/…Boosting_Algorithm…/0deec524a8.
Abstract
Optimizing Buying Drugs Using Data mining
By
Mohammad Mahdi Toranji
Developing information technology’s application in health care systems leads to advantages including accessibility of data. Applying data mining methods on available data could improves management and decision making process. This study was aimed to evaluate various algorithms that have been used in data mining to define a model for prediction of medications utilization in hospitals. For this purpose we extracted data from health information system of Bam’s Pasteur hospital that was saved for 5 years. Models such as LSSVR, LR, BAGTREE, ADABOOST, SVR and MLP were evaluated in prediction of drug usage. Power of mentioned models for prediction was assessed according to MAE, RMSE, MSE and R2 measures. In conclusion BAGTREE model was revealed as best model.
Keywords : Hospital Information Systems, Buying Drugs, Prediction, Pharmacy
IN THE NAME OF GOD
Optimizing Buying Drugs Using Data mining
BY
Mohammad Mahdi Toranji
THESIS
SUBMITTED TO THE SCHOOL OF GRADUATE STUDIES IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER
OF SCIENCE (MSc.)
IN
Information Technology Engineering (e-Commerce)
SHIRAZ UNIVERSITY
SHIRAZ
ISLAMIC REPUBLIC OF IRAN
EVALUATED AND APPROVED BY THE THESIS COMMITTEE AS:
………………………… , Ph.D., PROF. Of (CHAIRMAN)
…….…………………… , PhD., PROF . Of
………………………… , Ph.D., ASSOCIATEPROF Of
Information Technology Engineering
January 2015
Shiraz University
Faculty of eLearning
M.S. Thesis
In Information Technology Engineering (ECommerce)
Optimizing Buying Drugs Using Data mining
By
Mohammad Mahdi Toranji
Supervised by
Dr. Reza Boostani
January 2015
1.Association
2.Prediction
3.Classification
Clustering 4.
5 hospital information system
6knowledge discovery in database (KDD)
7 Electronic Patient Record
8 Laboratory information system
9. Artificial Neural Network
10.Support Vector Machine
11.Support Vector Regression
12 Multi Layer Neural Network
13 Support Vector Regression
14 Bagging trees
15 Linear Regression
16 Least Square Support Vector Regression
17 Mean Absolute Error
18 Mean Square Error
19 Root Mean Square Error
20 Mean Absolute Percentage Error
21 Coefficient of Determination
22 Train
23 Test
—————
————————————————————
—————
————————————————————
ت‌

تکه های دیگری از این پایان نامه را می توانید

در شماره بندی فوق بخوانید

متن کامل پایان نامه ها در سایت homatez.com موجود است

You may also like...

Add a Comment