ORIGINAL_ARTICLE
Sparse Representation-Based Classification (SRC): A Novel Method to Improve the Performance of Convolutional Neural Networks in Detecting P300 Signals
Introduction: Brain-Computer Interface (BCI) offers a non-muscle way between the humanbrain and the outside world to make a better life for disabled people. In BCI applicationsP300 signal has an effective role; therefore, distinguishing P300 and non-P300 componentsin EEG signal (i.e. P300 detection) becomes a vital problem in BCI applications. Recently,Convolutional Neural Networks (CNNs) have had a significant application in detection ofP300 signals in the field of BCIs. The P300 signal has low Signal to Noise Ratio (SNR). Onthe other hand, the CNN detection rate is so sensitive to SNR; therefore, CNN detection ratedrops dramatically when it is faces with P300 data. In this study, a novel structure is proposed to improve the performance of CNN in P300 signal detection by means of improving its performance against low SNR signals.Methods: In the proposed structure, Sparse Representation-based Classification (SRC) wasused as the first substructure. This block is responsible for prediction of the expected P300signal among artifacts and noise. The second substructure performed P300 classification with Adadelta algorithm. Thanks to such SNR improvement scheme; the proposed structure i able to increase the rate of accuracy in the field of P300 signal detection.Results: To evaluate the performance of the proposed structure, we applied it on EPFLdataset for P300 detection, and then the achieved results were compared with those obtained from the basic CNN structure. The comparisons revealed the superiority of the proposed structure against its alternative, so that its True Positive Rate (TPR) was promoted about 19.66%. Such improvements for false detections and accuracy parameters were 1.93% and 10.46%, respectively, which show the effectiveness of applying the proposed structure in detecting P300 signals.Conclusion: The better accuracy of the proposed algorithm compared to basic CNN, inparallel with its more robustness, showed that the Sparse Representation-based Classification (SRC) had a considerable potential to be used as an improving idea in CNN-based P300 detection.Keywords: EEG, Neural Networks, Signal Detection, Machine Learning, Brain-ComputerInterfaces, Brain-Computer Interface, Brain, Neuroscience, P300, Convolutional NeuralNetworks, Deep Learning
https://jhmi.sums.ac.ir/article_45380_906b46bdd2c2b2acc2d3408b9c8b1fb3.pdf
2019-04-01
37
46
Seyed Vahab
Shojaedini
shojaeddini_va@yahoo.com
1
LEAD_AUTHOR
Sajedeh
Morabbi
report.classic@gmail.com
2
AUTHOR
Espinosa R. Increased Signal to Noise Ratio in P300 Potentials by the Method of Coherent Self-Averaging in BCI Systems. International
1
Scholarly and Scientific Research & Innovation. 2013;7(11):386-90.
2
Cecotti H, Graser A. Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE Trans Pattern Anal Mach Intell. 2011;33(3):433-45. doi: 10.1109/ TPAMI.2010.125.
3
da Silva-Sauer L, Valero-Aguayo L, de la Torre- Luque A, Ron-Angevin R, Varona-Moya S. Concentration on performance with P300-based BCI systems: a matter of interface features.
4
Appl Ergon. 2016;52:325-32. doi: 10.1016/j. apergo.2015.08.002.
5
Mubeen MA, Knuth KH. Evidence-Based Filters for Signal Detection: Application to Evoked Brain Responses. arXiv preprint arXiv:11071257. 2011.
6
Alvarado-Gonzalez M, Garduno E, Bribiesca E, Yanez-Suarez O, Medina-Banuelos V. P300 Detection Based on EEG Shape Features. Comput Math Methods Med. 2016;2016:2029791. doi:
7
1155/2016/2029791.
8
Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface.
9
IEEE Trans Rehabil Eng. 2000;8(2):174-9.doi: 10.1109/86.847808.
10
Vareka L, Mautner P, editors. Using the Windowed means paradigm for single trial P300 detection. 2015 38th International Conference
11
on Telecommunications and Signal Processing (TSP); 2015: IEEE.doi: 10.1109/tsp.2015.7296414.
12
Hutagalung SS, Turnip A, Munandar A, editors. P300 detection based on extraction and classification in online BCI. 2013 3rd
13
International Conference on Instrumentation Control and Automation (ICA); 2013: IEEE.doi: 10.1109/ica.2013.6734042.
14
Sobhani A, editor P300 classification using deep belief nets. European Symposium on Artificial Neural Networks (ESANN); 2014.
15
Magee R, Givigi S, editors. A genetic algorithm for single-trial P300 detection with a lowcost EEG headset. 2015 Annual IEEE Systems
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Conference (SysCon) Proceedings; 2015. doi:
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1109/syscon.2015.7116757.
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Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT press; Vol. 1. 2016.
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Maddula R, Stivers J, Mousavi M, Ravindran S, de Sa V, editors. Deep recurrent convolutional neural networks for classifying P300 BCI signals. Proceedings of the Graz BCI Conference; 2017.
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23
Geraci JR, Kapoor P. A method of limiting performance loss of CNNs in noisy environments. arXiv preprint arXiv:170200932. 2017.
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Hoffmann U, Vesin JM, Ebrahimi T, Diserens K. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci
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Zhang W, Itoh K, Tanida J, Ichioka Y. Parallel
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optimization algorithms. arXiv preprint
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arXiv:160904747. 2016.
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Kundu S, Ari S, editors. P300 Detection Using Ensemble of SVM for Brain-Computer Interface Application. 2018 9th International Conference on Computing, Communication and Networking
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Technologies (ICCCNT); 2018: IEEE.doi: 10.1109/ icccnt.2018.8493903.
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Manyakov NV, Chumerin N, Combaz A, Van Hulle MM. Comparison of classification methods for P300 brain-computer interface on disabled subjects. Comput Intell Neurosci. 2011;2011:519868. doi: 10.1155/2011/51986.
41
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42
ORIGINAL_ARTICLE
Identification of Prerequisites for the Deployment of Business Process Management Practices in Iran’s Hospitals
Introduction: Business Process Management (BPM) is a disciplined approach that allows abusiness to identify, model, deploy, execute, manage, monitor, and improve its processes in a standardized manner. This research aimed to identify the prerequisites for the deployment of this approach in Iran’s Hospitals.Methods: The present research was a qualitative cross-sectional study which was conductedusing the content analysis method in 2017. Sampling was performed using the purposivesampling method and continued until data saturation. The participants were 18 men and5 women. The data were collected through semi-structured interviews. Data analysis wasperformed using the content analysis method.Results: After analyzing the contents of the interviews, we classified the prerequisites forthe deployment of BPM practices into six themes and 14 subthemes: Process Engineering,Flexible Treatment Guidelines and Procedures, Flexible Organizational Rules, LearningOrganization, Smart Electronic Filing, and Access Control Systems.Conclusion: According to the experts interviewed, decision-makers have to carefullyaddress the prerequisites such as legal and cultural requirements and the limitations such asbudgetary constraints before initiating the deployment of BPM systems. Overall, it appearsthat the localization and deployment of this approach, as much as it is currently possible, can benefit the Iranian healthcare systems as well as Iranian patients.Keywords: Process, Quality Improvement, Healthcare, Business Process Management
https://jhmi.sums.ac.ir/article_45381_d4ace95d0fcb88c28a72b3fb1621395a.pdf
2019-04-01
47
55
Farzaneh
Doosty
farzanehdoosty@gmail.com
1
Ph.D of Health Services Management, Health Management and Economics Research Center, Iran University of Medical
Sciences, Tehran, Iran
AUTHOR
Vahid
Rasi
vahidrasi65@gmail.com
2
Ph.D Student of Health Services Management, School of health management and information sciences, Iran university of
medical sciences, Tehran, Iran
LEAD_AUTHOR
Mohammad
Yarmohammadian
3
Professor, Health Management and Economic Research Center, Health Services Administration, Isfahan University of
Medical Sciences, Isfahan, Iran
AUTHOR
Mohsen
Sadeghpour
4
Ph.D Student of Educational Management. Manager of organizational development & administrational evolution,
Mashhad University of Medical Sciences, Mashhad, Iran
AUTHOR
Yarmohammadian MH, Ebrahimipour H,
1
Doosty F. Improvement of hospital processes through business process management in Qaem Teaching Hospital: A work in progress. Journal of education and health promotion. 2014;3:111. doi: 10.4103/2277-9531.145902.
2
Yarmohammadian MH, Ebrahimipour H, Doosty F. Developingâ an integrated business process management and leanâ model for
3
improving quality of services in teaching hospitals. International Journal of Health System and Disaster Management. 2013;1(4):229. doi: 10.4103/2347-9019.130743.
4
Doosty F Y, Mohammad H,, Ebrahimipour H. Introduction to Business Process Management Approach in Health Organizations. Health Information Management. 2012;9(7)87-99. Persian.
5
Yarmohammadian M, Khosravizadeh O, Alirezaei S, Doosty F. Impact of Business Process Model on Improving the Discharge Process in Teaching Hospital of Ghaem, Mashhad. Journal of Health Administration. 2015;18(61):47-56.
6
Gabryelczyk R, Jurczuk A, Roztocki N, editors. Business process management in transition economies: current research landscape and future opportunities. Proceedings of the 22th Americas Conference on Information Systems (AMCIS); 2016.
7
Lenz R, Reichert M. IT support for healthcare processesâpremises, challenges, perspectives. Data & Knowledge Engineering. 2007;61(1):39-58.
8
doi: 10.1016/j.datak.2006.04.007.
9
Kohlbacher M. The effects of process orientation: a literature review. Business Process Management Journal. 2010;16(1):135-52. doi: 10.1108/14637151011017985.
10
Braubach L, Pokahr A, Jander K, Lamersdorf W, Burmeister B. Go4flex: Goal-oriented process
11
modelling. Intelligent Distributed Computing IV: Springer; 2010. p. 77-87.doi: 10.1007/978-3- 642-15211-5_9.
12
Sun Y, Su J, editors. Computing degree of parallelism for BPMN processes. International Conference on Service-Oriented Computing;
13
: Springer.doi: 10.1007/978-3-642-25535-9_1
14
Jeston J. Business process management. Abingdon:
15
Routledge; 2014. doi: 10.4324/9780203081327.
16
Zur Muehlen M, Shapiro R. Business process analytics. Handbook on Business Process Management 2. New York: Springer; 2010. p.
17
Dumas M, La Rosa M, Mendling J, Reijers HA. Fundamentals of business process management. New York: Springer; 2013.
18
Lim ET, Pan SL, Tan CW. Managing user acceptance towards enterprise resource planning (ERP) systemsâunderstanding the dissonance between user expectations and managerial
19
policies. European Journal of Information Systems. 2005;14(2):135-49. doi: 10.1057/palgrave. ejis.3000531.
20
Fox J, Dunlop R, editors. Careflow: theory and practice. International Conference on Business Process Management; 2007: Springer.
21
Müller R, Rogge-Solti A, editors. BPMN for healthcare processes. Proceedings of the 3rd Central-European Workshop on Services and their Composition (ZEUS 2011), Karlsruhe, Germany; 2011.
22
Mukkamala RR. A Formal Model For Declarative Workflows. København: IT University of Copenhagen; 2012.
23
El-Hassan O, Fiadeiro JL, Heckel R, editors. Managing socio-technical interactions in healthcare systems. International Conference on Business Process Management; 2007: Springer.
24
Godin G, Belanger-Gravel A, Eccles M, Grimshaw J. Healthcare professionalsâ intentions and behaviours: a systematic review of studies based on social cognitive theories. Implementation
25
science : IS. 2008;3:36. doi: 10.1186/1748-5908-3-36.
26
Mans RS, Schonenberg M, Song M, van der Aalst WM, Bakker PJ, editors. Application of process mining in healthcareâa case study in a
27
dutch hospital. International joint conference on biomedical engineering systems and technologies; 2008: Springer.doi: 10.1007/978-3- 540-92219-3_32.
28
Ghattas J, Soffer P, Peleg M. Improving business process decision making based on past experience. Decision Support Systems. 2014;59:93-107. doi: 10.1016/j.dss.2013.10.009.
29
Schnipper JL, Gandhi TK, Wald JS, Grant RW, Poon EG, Volk LA, et al. Design and implementation of a web-based patient portal linked to an electronic health record designed to improve medication safety: the Patient Gateway medications module. Inform Prim Care. 2008;16(2):147-55. doi: 10.14236/jhi.v16i2.686.
30
Gooch P, Roudsari A. Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for processoriented health information systems. J Am Med
31
Inform Assoc. 2011;18(6):738-48. doi: 10.1136/ amiajnl-2010-000033.
32
Chen P. Goal-oriented business process monitoring: An approach based on user requirement notation combined with business
33
intelligence and web services: Carleton University; 2008.
34
Alhaqbani B, Fidge C, editors. Access control requirements for processing electronic health records. International Conference on Business Process Management; 2007: Springer.
35
Gajanayake R, Iannella R, Sahama T. Privacy oriented access control for electronic healt records. Electronic Journal of Health Informatics. 2014;8(2):15.
36
Bahga A, Madisetti VK. A cloud-based approach for interoperable electronic health records (EHRs). IEEE journal of biomedical and health
37
informatics. 2013;17(5):894-906. doi: 10.1109/ JBHI.2013.2257818.
38
Li M, Yu S, Ren K, Lou W. Securing personal health records in cloud computing: Patientcentric and fine-grained data access control in
39
multi-owner settings. International conference on security and privacy in communication systems 2010:89-106. doi: 10.1007/978-3-642-16161-2_6.
40
Ammon Rv. Event-driven business process management. Encyclopedia of Database Systems. 2009:1068-71.
41
AlShathry O. Business process management: a maturity assessment of Saudi Arabian organizations. Business Process Management Journal. 2016;22(3):507-21. doi: 10.1108/bpmj-07-
42
Rohani S, Zaree Ravasan A. Critical Success Factors for the Implementation of Business Process Management Systems. Informatic Health Management. 2015;3(12):53-76.
43
Kalhori A, Haji-Heidari N. Factors Influencing Business Process Management Implementation Case Study: Petrochemical Commerce Corporation. Quarterly Journal of Management and Development Process. 2012;25(2):149-77.
44
Rezaie K, Tadayoun S, Ostadi B, Aghdasi M. Critical Success Factors in implementation of Process Management and a Framework for Assessment of organization Readiness. Industrial Management Journal. 2009;1(3):37-52.
45
Santos H, Alves C, Santos G, Santana A. Identifying strategies for managing critical success factors of BPM initiatives in Brazilian Public Organisation. Trilhas Tecnicas. 2014;1(1).
46
Soltani Delgosha M, Shafiee A. Identification and extraction of the components of business process management governance using the
47
Meta-synthesis method. Journal of Management Sciences of Iran. 2013;8(30):127-46.
48
Haji sadeghi B NB, Ranjbar M. Measuring Scale Preparing for the implementation of a business process management system at Tehran Regional Electricity Company. Quarterly Journal of Improvement Management Studies. 2010;183-201.
49
ORIGINAL_ARTICLE
Investigating and Modeling the significant reasons of Percutaneous Coronary Intervention patients to participate rarely in cardiac rehabilitation - A data mining approach
Objective: The high prevalence of cardiovascular diseases has caused many health problems in countries. Cardiac Rehabilitation Programs (CRPs) is a complementary therapy for Percutaneous Coronary Intervention (PCI) patients. However, PCI patients hardly attend CRPs. This study aims to decipher the reasons why PCI patients rarely participate in CRPs after PCI.Methods: The parameters affecting the attendance of the patients at CRPs were identified by using the previous studies and opinions of experts. A questionnaire was designed based on the identified parameters and distributed among PCI patients who were referred to Tehran Heart Center Hospital.Results: According to data mining approach, 184 samples were collected and classified with three algorithms (Decision Trees, k-Nearest Neighbor (kNN), and Naïve Bayes). The obtained results by decision trees were superior with the average accuracy of 82%, while kNN and Naïve Bayes obtained 81.2% and 78%, respectively. Results showed that lack of physician’s advice was the most significant reason for non-participation of PCI patients in CRPs (P< .0001). Other factors were family and friends’ encouragement, paying expenses by insurance, awareness of the benefits of the CRPs, and comorbidity, respectively.Conclusion: Results of the best model can enhance the quality of services, promote health and prevent additional costs for patients. Keywords: Cardiovascular Disease, Percutaneous Coronary Intervention, Cardiac Rehabilitation Programs, Data Mining, Classification
https://jhmi.sums.ac.ir/article_45382_3c98007408e7548b8563c3ccd9c0024c.pdf
2019-04-01
56
65
Tara
Zamir
1
Faculty of Industrial and Systems Engineering (IT Engineering Group), Tarbiat Modares University, Tehran, Iran.
AUTHOR
Mohammad Mehdi
Sepehri
s.tara.zamir@gmail.com
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
LEAD_AUTHOR
Hassan
Aghajani
aghajanihas@yahoo.com
3
Department of Cardiology, Tehran University of Medical Sciences, Tehran, Iran.
AUTHOR
Morteza
Khakzar Bafruei
khakzar@gmail.com
4
Department of Industrial Engineering, Technology Development Institute (ACECR), Tehran, Iran.
AUTHOR
Toktam
Khatibi
toktamk@gmail.com
5
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
AUTHOR
Yu RX and Muller-Riemenschneider F. Effectiveness of exercise after PCI in the secondary prevention of coronary heart disease: A systematic review. Eur J Integr Med 2011; 3: e63-e69.
1
Iran: What causes the most deaths? What causes the most premature death?
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Lavie CJ and Milani RV. Cardiac Rehabilitation and Exercise Training in Secondary Coronary Heart Disease Prevention. Prog Cardiovasc Dis 2011; 53: 397â403.
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Magalhaes S, Ribeiro MM, Barreira A, et al. Long-term effects of a cardiac rehabilitation program in the control of cardiovascular risk factors. Rev Port Cardiol 2013; 32: 191-199.
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Cui F, Ren Y, Jin H, et al. Rehabilitation training improves exercise tolerance after
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percutaneous coronary intervention. J Biomed Res 2012; 26: 248-252.
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Giannuzzi P, Saner H, Bjornstad H, et al. Secondary Prevention Through Cardiac Rehabilitation Position Paper of the Working Group on Cardiac Rehabilitation and Exercise Physiology of the European Society of Cardiology. Eur Heart J 2003; 24: 1273-1278.
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Menezes AR, Lavie CJ, Milani RV, et al. Cardiac Rehabilitation in the United States. Prog Cardiovasc Dis 2014; 56: 522â529.
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Blumenthal JA, Sherwood A, Smith PJ, Watkins L, Mabe S, Kraus WE, Ingle K, Miller P & Hinderliter A. Enhancing Cardiac Rehabilitation with Stress Management Training. Circulation 2016; 133: 1341-1350.
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Herber OR, Smith K, White M, & Jones MC. âJust not for meâ â contributing factors to nonattendance/noncompletion at phase III cardiac rehabilitation in acute coronary syndrome patients: a qualitative enquiry. J Clin Nurs 2017; DOI: 10.1111/jocn.13722.
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Aragam KG, Moscucci M, Smith DE, et al. Trends and disparities in referral to cardiac rehabilitation after percutaneous coronary intervention. Am Heart J 2011; 161: 544-551.
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Thomas RJ, King M, Lui K, et al. AACVPR/ACC/AHA 2007 Performance Measures on Cardiac Rehabilitation for Referral to and Delivery of Cardiac Rehabilitation/Secondary Prevention Services. J Am Coll Cardiol 2007; 50: 1400-1433.
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Galve E, Castero A, Cordero A, et al. Update in Cardiology: Vascular Risk and Cardiac Rehabilitation. Rev Esp Cardiol 2013; 66: 124-130.
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Banerjee AT, Grace SL, Thomas SG, et al. Cultural factors facilitating cardiac rehabilitation participation among Canadian South Asians: A qualitative study. Heart & Lung 2010; 39: 494â503.
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Clark AM, King-Shier KM, Thompson DR, et al. A qualitative systematic review of influences on attendance at cardiac rehabilitation programs after referral. Am Heart J 2012; 164: 835-845.
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Dunlay SM, Witt BJ, Allison TG, et al. Barriers to participation in cardiac rehabilitation. Am Heart J 2009; 158: 852-859.
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Lieberman L, Meana M and Stewart DE. Cardiac rehabilitation: Gender differences in factors influencing participation. J Womenâs Health 1998; 7: 717-723.
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Brown TM, Hernandez AF, Bittner V, et al. Predictors of Cardiac Rehabilitation Referral in Coronary Artery Disease Patients. J Am Coll Cardiol 2009; 54: 515-521.
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Gaalema DE, Cutler AY, Higgins ST, et al. Smoking and Cardiac Rehabilitation Participation: Associations with Referral, Attendance and Adherence. Prev Med 2015; 80: 67-74.
22
Gaalema DE, Savage PD, Rengo JL, et al. Patient Characteristics Predictive of Cardiac Rehabilitation Adherence. J Cardiopulm Rehabil Prev 2017; 37(2): 103-110.
23
Hand D, Mannila H and Smyth P. Principles of Data Mining. Cambridge, Massachusetts: The MIT Press, 2001.
24
Yoo I, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang J-F & Hua L. Data Mining in Healthcare and Biomedicine: A Survey of the Literature. J Med Syst 2012; 36: 2431-2448.
25
Lin WT, Wu YC, Zheng JS, et al. Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital. Expert Syst Appl 2011; 38: 11078â11084.
26
Hu PJH, Wei CP, Cheng TH, et al. Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making. Decis Support Syst 2007; 43: 1226-1241.
27
Jerez-Aragones JM, Gomez-Ruiz JA, Ramos-Jimenez G, et al. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med 2003; 27: 45-63.
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Harper PR and Winslett DJ. Classification trees: A possible method for maternity risk grouping. Eur J Oper Res 2006; 169: 146-156.
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Feng Y, Wu Z, Zhou X, et al. Knowledge discovery in traditional Chinese medicine: State of the art and perspectives. Artif Intell Med 2006; 38: 219-236.
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Ltifi H, Ben Mohamed E and ben Ayed M. Interactive visual knowledge discovery
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from data-based temporal decision support system. Info Vis 2016; 15: 31-50.
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King KM, Humen DP, Smith HL, et al. Predicting and explaining cardiac rehabilitation attendance. Can J Cardiol 2001; 17: 291-296.
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Clark AM, Sharp C and Macintyre PD. The role of age in moderating access to cardiac rehabilitation in Scotland. Ageing and Society 2002; 22: 501-515.
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Leung YW, Brual J, Macpherson A, et al. Geographic issues in cardiac rehabilitation utilization: A narrative review. Health & Place 2010; 16: 1196-1205.
36
Tod AM, Lacey EA and McNeill F. 'I'm still waiting...': barriers to accessing cardiac rehabilitation services. J Adv Nurs 2002; 40: 421-431.
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Grace SL, Abbey SE, Shnek ZM, et al. Cardiac rehabilitation I: review of psychosocial factors. Gen Hosp Psychiat 2002; 24: 121-126.
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Han J, Kamber M and Pei J. Data Mining Concepts and Techniques. 3rd ed. MA, USA: Elsevier, 2012.
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ORIGINAL_ARTICLE
Evaluating hospital performance using an integrated balanced scorecard and fuzzy data envelopment analysis
Introduction: Hospitals are considered as the most important consumer units in thehealthcare sector and are one of the main organizations providing health care services.Therefore, efficiency assessment is very important in hospital sectors. Besides, in order tobe able to develop and compete, hospitals need a performance evaluation system to evaluate the efficiency and effectiveness of their programs, processes, and human resources. The aim of this paper was to assess the efficiency of hospitals by a combined model of balanced scorecard-fuzzy data envelopment analysis (BSC-fuzzy DEA).Methods: The present study was a descriptive-analytical study that was conducted to assess the efficiency of 8 hospitals in Qazvin province in 2018. The required data were collected through historical data and a questionnaire. 30 experts, including hospital managers and staff, and patients were randomly chosen to collect data in each hospital. The methods used in this study were balanced scorecard (BSC) for determining performance indicators in hospitals and fuzzy data envelopment analysis for assessing the efficiency score of hospitals. Data were analyzed by GAMS software version 23.5.1.Results: The results of applying fuzzy DEA revealed that Amiralmomenin Hospital, Bu AliClinic, and 22 Bahman Hospital have the best performances among Qazvin hospitals. Thetechnical efficiency scores of these hospitals under the uncertainty level of α=0.75 are 1.72,1.58, and 1.53, respectively.Conclusion: The use of BSC measures in four perspectives of customer, financial, internalprocesses and growth, and innovation reflects the overall strategic objectives of the hospitals in the performance evaluation process. Furthermore, applying the BSC and fuzzy DEA methods provides a comprehensive performance assessment tool for hospitals, and helps decision makers to obtain more accurate planning to expand the capacity of health services and save the resources.Keywords:Hospitals, Balanced scorecard, Performance, Indicator
https://jhmi.sums.ac.ir/article_45383_47f3dc72a704433f5199a37b6c2ada47.pdf
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Seyed Morteza
Hatefi
smhatefi@alumni.ut.ac.ir
1
Faculty of Engineering, Shahrekord University, Shahekord, Iran
LEAD_AUTHOR
Abdorrahman
Haeri
ahaeri@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science & Technology (IUST), Tehran, Iran
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ORIGINAL_ARTICLE
Identifying and Prioritizing the Effective Factors on Establishing Accreditation System in Tehran Hospitals affiliated with the Social Security Organization in Tehran, 2016
Introduction: There is much less attention to the structural, processing, and functionalstandards in accreditation of health care organizations. The purpose of this study was todetermine and prioritize the factors affecting the implementation of accreditation system inhospitals affiliated with the Social Security Organization in Tehran in 2016.Methods: This is a cross‐sectional quantitative study conducted among hospital staffrecruited through census sampling. To collect the data, a researcher-made questionnaireconsisting of 24 factors was designed using hierarchical analysis method. After collecting thequestionnaires, studying criteria and factors were analyzed and prioritized based on AnalyticHierarchy Process model (AHP) and inconsistency ratio (ICR) using the Super DecisionsSoftware. To determine whether there is a significant difference between the respondents’answers, we performed one-sample t-test using SPSS software.Results: According to the findings, 49 out of the 170 participants were male and the restwere female. In order to investigate the factors affecting the establishment of the accreditation system, we the ranking of factors showed that the output criterion with the weight of 0.443 had the highest priority, and then the criterion of the structure with a weight of 0.279 and the process criterion with a weight of 0.278 in the next priorities were placed.Conclusion: The findings of the present study, scientifically through the review of documentsand evidence, as well as their integration with the opinions of domestic experts, resulted inachieving an effective model for establishing accreditation based on structural, processing,and output standards and considering the weight of each group of standards. The factorsaffecting the accreditation system take into account the constraints on the content andimplementation process of the current accreditation program and complements the existinggaps by adding the dimensions and components required. Using a simple, comprehensiveand efficient approach, it is possible to provide an opportunity to improve the status ofaccreditation and quality of services in hospitals of Tehran’s social security hospitals.Keywords: Accreditation, Donabedian Model, Hospital, Social Security
https://jhmi.sums.ac.ir/article_45384_36773a401cf4cf38e3bab88154d78974.pdf
2019-04-01
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Zahra
Ebrahim
zahra.ebrahim.1366@gmail.com
1
PhD candidate in Health care services management, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Amir Ashkan
Nasiripour
nasiripour@srbiau.ac.ir
2
Associate Professor ,Department of Health Services Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
LEAD_AUTHOR
Pouran
Raeissi
raeissi2009@yahoo.com
3
Professor ,Department of Health Management, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
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