The Fuzzy Logic Modeling of Solar Air Heater Having Conical Springs Attached on the Absorber Plate

Year 2019, Volume: 22 Issue: 4, 847 - 853, 01.12.2019

Mehmet İbrahim Talan Mehmet Rahmi Canal Veysel Alcan Hilal Kaya Murat Zinnuroğlu

https://doi.org/10.2339/politeknik.453830

Abstract

Physical
examination, clinical tests and electrophysiological methods are used in the
diagnosis of carpal tunnel syndrome (CTS). However, in practice there are no
standard clinical and electrophysiological tests for clinics and laboratories.
Therefore, data fragmentation or incompatibilities may occur in Electronic
Health Record (EHR) systems. Furthermore, secondary use and different
biomedical research targets are not considered in these EHR systems. During
routine documentation, incomplete, incorrect, inconsistent data entry and
incorrect coding can be done. This study aimed to develop an EHR system that
could be used in different clinics and centers in diagnosis of CTS, thus
creating a standardized, high quality, predictive, preventive, personalized and
real-time participatory CTS biomedical data warehouse. The CTS-based EHR system
was developed using Microsoft Visual Studio C # programming language. Also
during a new patient record, the system was supported by a clinical decision
support system (CDSS) based on the data mining methods using WEKA program for
pre-diagnosis of the CTS. This EHR system also allows clinical and
electrophysiological test results as well as genetic and environmental variants
to be integrated into a single database within the framework of precision
medicine approachment. In addition, this system can provide a large scale
accurate and complete data warehouse for secondary use purposes.

Keywords

Carpal tunnel syndrome, electronic healt record system, data warehouse, clinical decision support system

Karpal Tünel Sendromu Temelli Elektronik Sağlık Kayıt Sisteminin Geliştirilmesi

Abstract

Karpal tünel sendromunun (KTS)
tanısında, fiziksel muayene, klinik testler ve elektrofizyolojik yöntemler
kullanılmaktadır. Fakat pratikte uygulanan klinik ve elektrofizyolojik
testlerde klinik ve laboratuvarlar için bir standart bulunmamaktadır. Bundan
dolayı Elektronik Sağlık Kaydı (ESK) sistemlerinde, veri parçalanması veya
uyumsuzluklar meydana gelebilmektedir. Ayrıca bu ESK sistemlerinde, ikincil
kullanım ve farklı biyomedikal araştırma hedefleri dikkate alınmamakta ve rutin
dökümantasyon işlemi sırasında, eksik, hatalı, tutarsız veri girişleri ve
hatalı kodlamaları yapılabilmektedir. Bu çalışma ile, KTS tanısında farklı
klinik ve merkezlerce de kullanılabilecek bir ESK sisteminin geliştirilmesi ve
böylelikle standartlaştırılmış, kaliteli, öngörücü, önleyici,
kişiselleştirilmiş ve gerçek zamanlı katılımcı bir KTS biyomedikal veri
ambarının oluşturulması hedeflenmiştir. KTS tabanlı ESK sistemi, Microsoft
Visual Studio C# programlama dili kullanılarak geliştirilmiştir. Ayrıca; yeni
hasta kaydı esnasında KTS ön tanısı için WEKA programı kullanılarak veri
madenciliği yöntemine dayalı bir klinik karar destek sistemi (KKDS) ile
desteklenmiştir. Geliştirilen ESK sistemi, klinik ve elektrofizyoloijk test
sonuçlarının yanısıra hassas tıp yaklaşımı çerçevesinde genetik ve çevresel
varyantların da tek bir veri tabanına entegre edilmesine imkan tanımakta ve
ikincil kullanım amacıyla geniş ölçekli doğru eksiksiz ve aynı standartta bir
veri ambarı sunabilmektedir. 

Keywords

veri ambarı, Karpal tunel sendromu, elektronik hasta kayıt sistemi, klinik karar destek sistemi

References

• [1] Atroshi I., Gummesson C, Johnsson R., Ornstein E., Ranstam J. Rosen I., “Prevalence of carpal tunnel syndrome in a general population”, JAMA, 282: 153-8, (1999)
• [2] Nordstrom D.L., DeStefano F., Vierkant R.A., Layde P.M., “Incidence of diagnosed carpal tunnel syndrome in a general population”, Epidemiology, 9: 342-5, (1998)
• [3] Ghavanini M.R., Haghighat M., “Carpal tunnel syndrome: reappraisal of five clinical tests”, Electromyogr Clin Neurophysiol, 38: 437-41, (1998)
• [4] Gellman H., Gelberman R.H., Tan A.M., Botte M.J., “Carpal tunnel syndrome. An evaluation of the provocative diagnostic tests”, J Bone Joint Surg Am, 68: 735-7, (1986)
• [5] Tetro A.M., Evanoff B.A., Hollstien S.B., Gelberman R.H., “A new provocative test for carpal tunnel syndrome. Assessment of wrist flexion and nerve compression” J Bone Joint Surg Br, 80: 493-8, (1998)
• [6] Levine D.W., Simmons B.P., Koris M.J. et al., “A self-administered questionnaire for the assessment of severity of symptoms and functional status in carpal tunnel syndrome”, J Bone Joint Surg Am, 75:1585–92, (1993)
• [7] Jablecki C.K., Andary M.T., So Y.T., Wilkins D.E., Williams F.H., “Literature review of the usefulness of nerve conduction studies and electromyography for the evaluation of patients with carpal tunnel syndrome”, Muscle Nerve, 16:1392-1414, (1993)
• [8] Aminoff M.J., “Electromyography in Clinical Practice 2th ed”, Churchill Livingstone Inc, USA, (1987)
• [9] Bodofsky E.B., “Diagnosing mild carpal tunnel syndrome with interpolation”, Electromyogr Clin Neurophysiol, 44:379-383, (2004)
• [10] Kohara N., “Clinical and Electrophysiological Findings in Carpal Tunnel Syndrome”, Brain Nerve, 59:1229-1238, (2007)
• [11] Shortliff E.H. and Climine J.J., “Biomedıcal Informatics: Computer Applications In Health Care & Bıomedicine”, Springer, Newyok USA, (2006)
• [12] Gubbia J., Buyya R., Marusica S., Palaniswami M., “Internet of Things (IoT): A vision, architectural elements, and future directions”, Future Generation Computer Systems, 29;1645-1660, (2013)
• [13] Hanauer D.A., Ramakrishnan N., “Modeling temporal relationships in large scale clinical associations”, J Am Med Inform Assoc, 20:332-41, (2013)
• [14] Mullins I.M., Siadaty M.S., Lyman J., et al., “Data mining and clinical data repositories: insights from a 667,000 patient data set”, Comput Biol Med, 36:1351-77, (2006)
• [15] Wright A., Chen E.S., Maloney F.L., “An automated technique for identifying associations between medications, laboratory results and problems”, J Biomed Inform, 43:891-901, (2010)
• [16] Hoffman S., and Podgurski A., “Big Bad Data: Law, Public Health, and Biomedical Databases”, J.L. Med. & Ethıcs, 41:56-60, (2013)
• [17] Jensen P.B., Jensen L.J., Brunak S., “Mining electronic health records: towards better research applications and clinical care”, Nat Rev Genet, 13:395-405, (2012)
• [18] Ramakrishnan N., Hanauer D., Keller B., “Mining Electronic Health Records”, Computer, 43:77-81, (2010)
• [19] Kalra D., Musen M., Smıth B., Ceusters W., “Argos Policy Brief on Semantic Interoperability”, Studies in health technology and informatics, 170:1-15, (2011)
• [20] Phan J.H., Quo C.F., Cheng C., Wang M.D., “Multiscale integration of omic, imaging, and clinical data in biomedical informatics”, IEEE Rev Biomed Eng 5:74-87, (2012)
• [21] Talan M.I., Canal M.R., Zinnuroğlu M., Alcan V., “Tıbbi Veri Ambarı Kullanarak Veri Madenciliği Sınıflandırma Algoritmalarının Karşılaştırılması”, 1st International Turkish World Engineering and Science Congress, Antalya, 7-10, (2017)
• [22] Hakim A.J., Cherkas L., El Zayat S., MacGregor A.J., Spector T.D., “The genetic contribution to carpal tunnel syndrome in women: a twin study”, Spector Arthritis Rheum, 47:275-279, (2002)
• [23] Lozano-Calderón S., Anthony S., Ring D., “The quality and strength of evidence for etiology: example of carpal tunnel syndrome”, J Hand Surg Am, 33:525-538, (2008)
• [24] Bland J.D.P., “Carpal tunnel syndrome”, Curr Opin in Int Med, 4:578-82, (2005)
• [25] Becker J., Nora D.B., Gomes I., Stringari F.F., Seitensus R., Panosso J.S., “An evaluation of gender, obesity, age and diabetes mellitus as risk factors for carpal tunnel syndrome”, Clin Neurophysiol, 113:1429-34, (2002)
• [26] Lam N., Thurston A., “Association of obesity, gender, age and occupation with carpal tunnel syndrome”, Aust N Z J Surg, 68:190-3, (1998)
• [27] Reinstein L., “Hand dominance in carpal tunnel syndrome”, Arch Phys Med Rehabil, 62:202-3, (1981)
• [28] İnternet: The Precision Medicine Initiative, https://obamawhitehouse.archives.gov/node/333101 Son Erişim Tarihi: 15.08.2018.
• [29] Jameson J.L., Longo D.L., “Precision Medicine-Personalized, Problematic, and Promising”, Obstetrical & Gynecological Survey, 70:612-614, (2015)
• [30] Weiskopf N.G., Hripcsak G., Swaminathan S., Weng C., “Defining and measuring completeness of electronic health records for secondary use”, J Biomed Inform, 46:830-6, (2013)
• [31] Chan K.S., Fowles J.B., Weiner J.P., “Electronic Health Records and the Reliability and validity of quality measures: a review of the literature”, Med Care Res Rev, 67:503-27, (2010)
• [32] Sullivan P., Goldmann D., “The promise of comparative effectiveness research”, JAMA, 305:400-1, (2011)
• [33] Fernandes L., O’Connor M., Weaver V., “Big data, bigger outcomes: Healthcare is embracing the big data movement, hoping to revolutionize HIM by distilling vast collection of data for specific analysis”, J Ahıma, 83:38-43, (2012)
• [34] Harper E.M., “The economic value of health care data”, Nurs Adm Q, 37:105-8, (2013)
• [35] Kulynych J. and Greely H.T., “Clinical genomics, big data, and electronic medical records: reconciling patient rights with research when privacy and science collide”, J Law Biosci, 4:94-132, (2017)
• [36] Kayaalp M., “Patient Privacy in the Era of Big Data”, Balkan Med J, 35:8-17, (2018)