Volume 8, Issue 1 (Winter 2020)                   Iran J Health Sci 2020, 8(1): 29-39 | Back to browse issues page


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Khenarinezhad S, Mohammadzadeh N, GhaziSaeedi M, NaserMoghadasi A. Determination of Minimum Data Set for Designing a Diagnosis Decision Support System and Medication Follow-Up for Multiple Sclerosis. Iran J Health Sci 2020; 8 (1) :29-39
URL: http://jhs.mazums.ac.ir/article-1-717-en.html
School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran , ghazimar@tums.ac.ir
Abstract:   (1662 Views)
Background and Purpose: Diagnosis of multiple sclerosis (MS) is complicated because of the lack of definite factor. Decision support systems are expert systems which help physicians in decision-making process. First step in designing the system is identification of a minimum dataset (MDS). This study aimed to determine minimum dataset required to design diagnosis decision support system.
Materials and Methods: This research was a descriptive cross-sectional study. Data were gathered from medical guideline approved by Ministry of Health, Treatment and Medical Training, Multiple Sclerosis diagnosis, international guideline of Royal college of England, and McDonald Diagnostic criteria. Data collection tool was a designed checklist consisting of 100 items provided to 25 neurologists and MS fellowships of medical universities and private clinics in Iran.
Results: Out of 100 designed information’s items, 10 items were omitted due to CVR less than 0.49. Employment status items, history of MS in 3rd grade relatives, history of viral diseases, orbital MRI, optical coherence tomography, brain CT-scan, ESR, CRP, visually evoked potentials, delay duration of P100 for each eyes are all examples of information elements that have been omitted.
Conclusion: Determining the minimum dataset related to MS is an important step in designing diagnosis decision support system and medication follow-up. Therefore, MDSs can help those responsible for gathering standard information of patients with Multiple Sclerosis (MS), and causes improvement in management of information for this disease.
 
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Type of Study: Original Article | Subject: Emergency Medicine

References
1. Maghsoudi A. The study of prevalence of chronic diseases and its association with quality of life in the elderly of Ewaz (South of Fars province), 2014. Navid No. 2016;18(61):35-42.
2. Karimi S, Javadi M, Jafarzadeh F. Economic burden and costs of chronic diseases in Iran and the world. Health Inf Manag. 2012;8(7):984-96.
3. Gao J, Memmott B, Poulson J, Harmon B, Hammond C. Quantitative Ultrasound Imaging to Assess Skeletal Muscles in Adults with Multiple Sclerosis: A Feasibility Study. Journal of Ultrasound in Medicine. 2019;1(1):1-9. https://doi.org/10.1016/j.ultrasmedbio.2019.07.549 [DOI:10.1002/jum.14997]
4. Karussis D. The diagnosis of multiple sclerosis and the various related demyelinating syndromes: a critical review. Journal of autoimmunity.2014;48-49:134-42. [DOI:10.1016/j.jaut.2014.01.022] [PMID]
5. Bove R, McHenry A, Hellwig K, Houtchens M, Razaz N, Smyth P, et al. Multiple sclerosis in men: management considerations. Journal of neurology. 2016;263(7): 1263-73. [DOI:10.1007/s00415-015-8005-z] [PMID]
6. Belbasis L, Bellou V, Evangelou E, Ioannidis JP, Tzoulaki I. Environmental risk factors and multiple sclerosis: an umbrella review of systematic reviews and meta-analyses. The Lancet Neurology. 2015;14(3):263-73. [DOI:10.1016/S1474-4422(14)70267-4]
7. Ronicke S, Hirsch MC, Türk E, Larionov K, Tientcheu D, Wagner AD. Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet journal of rare diseases. 2019; 14(1):69. [DOI:10.1186/s13023-019-1040-6] [PMID] [PMCID]
8. Evans C, Beland S-G, Kulaga S, Wolfson C, Kingwell E, Marriott J, et al. Incidence and prevalence of multiple sclerosis in the Americas: a systematic review. Neuroepidemiology. 2013;40(3): 195-210. [DOI:10.1159/000342779] [PMID]
9. Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al. Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association. 2001; 8(6):527-34. [DOI:10.1136/jamia.2001.0080527] [PMID] [PMCID]
10. Shahin S, Eskandarieh S, Moghadasi AN, Razazian N, Baghbanian SM, Ashtari F, et al. Multiple sclerosis national registry system in Iran: validity and reliability of a minimum data set. Multiple sclerosis and related disorders. 2019;33:158-61. [DOI:10.1016/j.msard.2019.06.009] [PMID]
11. Mohammadi A, Ahmadi M, Gharagozlu A. Developing a minimum data set for an information management system to study traffic accidents in Iran. Iranian Red Crescent Medical Journal. 2016;18(3):e23677. [DOI:10.5812/ircmj.23677]
12. Zahmatkeshan M, Farjam M, Mohammadzadeh N, Noori T, Karbasi Z, Mahmoudvand Z, et al. Design of Infertility Monitoring System: Minimum Data Set Approach. Journal of medicine and life. 2019;12(1):56. [DOI:10.25122/jml-2018-0071] [PMID] [PMCID]
13. National Collaborating Centre for Chronic Conditions (UK). Multiple Sclerosis: National Clinical Guideline for Diagnosis and Management in Primary and Secondary Careclinical guideline for diagnosis and management in primary and secondary care. london: Royal College of Physicians of London; 2004. Available from: www.rcplondon. ac.uk.
14. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurology. 2018;17(2): 162-73. [DOI:10.1016/S1474-4422(17)30470-2]
15. Lawshe CH. A quantitative approach to content validity 1. Personnel psychology. 1975;28(4): 563-75. [DOI:10.1111/j.1744-6570.1975.tb01393.x]
16. Sharma AK, Gupta S. Neurological Disorder Diagnosis System. journal for research in applied science and engineering technology (ijraset) 2014;2(6): 296-301.
17. Linder R, Mörschner D, Pöppl S, Moser A. Computer-aided diagnosis of multiple sclerosis. Computational and Mathematical Methods in Medicine. 2009;10(1):39-47. [DOI:10.1080/17486700802070724]
18. Lublin FD. New multiple sclerosis phenotypic classification. European neurology. 2014;72 (Suppl. 1):1-5. [DOI:10.1159/000367614] [PMID]
19. Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83(3):278-86. [DOI:10.1212/WNL.0000000000000560] [PMID] [PMCID]
20. Borgohain R, Sanyal S. Rule based expert system for diagnosis of neuromuscular disorders. International journal Advanced Networking and Applications. 2012;04(01):1509-13.
21. Ayangbekun Oluwafemi J, Jimoh Ibrahim A. Expert system for diagnosis neurodegenerative disease. International journal of computer and information technology. 2015;4(4):694-8.
22. Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R, Weiner HL, et al. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS One. 2017;12(4):e0174866. [DOI:10.1371/journal.pone.0174866] [PMID] [PMCID]
23. Zdrodowska M, Dardzińska A, Chorąży M, Kułakowska A. Data mining techniques as a tool in neurological disorders diagnosis. acta mechanica et automatica. 2018;12(3):217-20. [DOI:10.2478/ama-2018-0033]
24. Daumer M, Neuhaus A, Lederer C, Scholz M, Wolinsky JS, Heiderhoff M. Prognosis of the individual course of disease-steps in developing a decision support tool for Multiple Sclerosis. BMC medical informatics and decision making. 2007;7(1):11. [DOI:10.1186/1472-6947-7-11] [PMID] [PMCID]
25. Chase HS, Mitrani LR, Lu GG, Fulgieri DJ. Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC medical informatics and decision making. 2017;17(1):24. [DOI:10.1186/s12911-017-0418-4] [PMID] [PMCID]
26. Agboizebeta IA, Chukwuyeni OJ. Cognitive analysis of multiple sclerosis utilizing fuzzy cluster means. International Journal of Artificial Intelligence & Applications. 2012;3(1):33-45. [DOI:10.5121/ijaia.2012.3103]
27. Ghahazi MA, Zarandi MF, Harirchian M, Damirchi-Darasi SR, editors. Fuzzy rule based expert system for diagnosis of multiple sclerosis. 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW); 2014: IEEE. [DOI:10.1109/NORBERT.2014.6893855]
28. Amooji A, Esmaeilpour Mianji N. A Fuzzy expert system for diagnosis of Multiple Sclerosis and Brain Tumor diseases. International Research Journal of Applied and Basic Sciences. 2015;9(11):2055-9.
29. Maleki M, Teshnehlab M, Nabavi M. Diagnosis of multiple sclerosis (MS) using convolutional neural network (CNN) from MRIs. Global Journal of Medicinal Plant Research. 2012;1(1):50-4.
30. Gaspari M, Roveda G, Scandellari C, Stecchi S. An expert system for the evaluation of EDSS in multiple sclerosis. Artificial intelligence in medicine. 2002;25(2):187-210. [DOI:10.1016/S0933-3657(02)00015-5]
31. Mathew S, Mathew S, Hamed HM, Qadri I. A web based decision support system driven for the neurological disorders. International journal of engineering research and general science. 2015;3(4):777-92.
32. Karaca Y, Hayta Ş. The significance of artificial neural networks algorithms classification in the multiple sclerosis and its subgroups. International Advanced Research Journal in Science, Engineering and Technology. 2015;2(12):1-7. [DOI:10.17148/IARJSET.2015.21201]

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