ALİ BULDU, KAZIM YILDIZ, EYÜP EMRE ÜLKÜ, ÖNDER DEMİR, Ufuk KURGAN

Kan Şekeri Sayacından Veri Toplama ve Anomali Tespiti

Kan şekeri hastalığı pandemik bir hastalık olarak kabul edilir; çünkü hasta sayısı her geçen gün artmaktadır. Kan şekeri hastalığının birçok nedeni vardır ve milyonlarca insan etkilenmektedir. Büyük şehirlerde ve hastanelerin yakınında yaşayan bazı insanlar sürekli olarak tedaviye sahip olabilir, ancak çoğu insan sağlık için çok önemli olan düzenli doktor kontrolünden yoksundur. Günlük kan şekeri ölçümlerini takip etmek için bazı araçlar vardır, ancak yalnızca kişisel kullanım içindir. Bu projede, çoklu hastalar için veri toplamak amacıyla kullanılan kişisel bir veri toplayıcı kullanılmıştır. Veriler bir veritabanında saklanır, daha sonra doktor veya hasta kendisi anlamlı bir grafikte ölçüm trendine ulaşabilir ve onu görebilir. Bu nedenle hastanelerde düzenli kontroller en aza indirilebilir ve ayrıca doktorlar hastalara tedaviden bahsedebilir. Bu çalışma ile kan şekeri verileri ölçüm cihazından bir kayıt ortamına aktarılır. Birçok cihazın bireysel kullanımı olmasına rağmen, bu proje ile bireysel kullanım için tasarlanmış bir sistem birden fazla hasta için kullanılır

Anahtar Kelimeler:

Bilgisayarla ölçme, Veri toplama, Diabet, Medikal bilgi sistemleri

Data collection from blood glucose meter and determination anomaly

Blood glucose disease is accepted as a pandemic disease because every day the patient count increases. There are many reasons for blood glucose disease and millions of people are affected. Some people living in big cities and near hospitals can have a continuous treatment, but most people are lack of regular doctor checking which is very important for the health. There some instruments to keep track of the daily blood glucose measurements but they are personal use only. In this project, a personal use only data collector used to collect data for multi patients. Data are stored in a database then doctor or the patient himself can reach and see the trend of the measurement in a meaningful graph. So, regular checking could be minimized in hospitals and also doctors can advise patients about treatment. With this study, blood glucose data is transferred from the measuring device to a recording medium. Although individual use of many devices, with this project a system designed for individual use is employed for more than one patient.

Keywords:

Diabetes, Data Collection, Computerized Instrumentation, Medical Information Systems,

PDF

___

Association, A. 2010. Standards of medical care in diabetes—2010. Diabetes Care 33(1): 11-61.
Buda, R., Addi, MM. 2014. A portable non-invasive blood glucose monitoring device. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), IEEE.
Bunescu, R., Struble, N., Marling, C., Shubrook, J., Schwartz, F. 2013. Blood glucose level prediction using physiological models and support vector regression. Machine Learning and Applications (ICMLA), 2013 12th International Conference on, IEEE.
Control, D., Group, CTR. 1993. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus.” N Engl J Med., 329(14): 977-986.
Duke, DL., Thorpe, C., Mahmoud, M., Zirie, M. 2008. Intelligent Diabetes Assistant: Using machine learning to help manage diabetes. Computer Systems and Applications, 2008. AICCSA 2008. IEEE/ACS International Conference on, IEEE.
Ferenci, T., Korner, A., Kovács, L. 2013. Correlation investigations between HbAlc and blood glucose indicators on type 1 diabetic Hungarian children. Applied Computational Intelligence and Informatics (SACI), 2013 IEEE 8th International Symposium on, IEEE.
Franciosi, M., Pellegrini, F., De Berardis, G., Belfiglio, M., Cavaliere, D., Di Nardo, B., Greenfield, S., Kaplan, SH., Sacco, M., Tognoni, G. 2001. The Impact of Blood Glucose Self-Monitoring on Metabolic Control and Quality of Life in Type 2 Diabetic Patients An urgent need for better educational strategies. Diabetes Care, 24(11): 1870-1877.
Izquierdo, RE., Knudson, PE., Meyer, S., Kearns, J., PloutzSnyder, R.,Weinstock, RS. 2003. A comparison of diabetes education administered through telemedicine versus in person. Diabetes Care, 26(4): 1002-1007.
Lisin, M., Joseph, J., Goyal, A. 2009. Microsoft SQL Server 2008 Reporting Services Unleashed, Sams Publishing. MacDonald, M., Freeman, A. 2010. Pro Asp. net 4 in C# 2010, Apress.
Manual, 2008. AccuChek Smart Pix Pocket Tools Germany, Roche.
Miele, F., Eccher, C.,Piras, EM. 2015. Using a Mobile App to Manage Type 1 Diabetes: The Case of TreC Diabetes. Proceedings of the 5th International Conference on Digital Health 2015, ACM.
Norris, SL., Engelgau, MM., Narayan, KV. 2001. Effectiveness of self-management training in type 2 diabetes a systematic review of randomized controlled trials. Diabetes Care, 24(3): 561-587.
Schiffrin, A.,Belmonte, M. 1982. Multiple daily self-glucose monitoring: its essential role in long-term glucose control in insulin-dependent diabetic patients treated with pump and multiple subcutaneous injections. Diabetes Care, 5(5): 479-484.
Smith, BK., Frost, J., Albayrak, M., Sudhakar, R. 2007. Integrating glucometers and digital photography as experience capture tools to enhance patient understanding and communication of diabetes self-management practices. Person. Ubiq. Comput., 11(4): 273-286.
Strowig, SM., Raskin, P. 1998. Improved glycemic control in intensively treated type 1 diabetic patients using blood glucose meters with storage capability and computer-assisted analyses. Diabetes Care, 21(10): 1694-1698.
Wang, Z., Paranjape, R. 2013. Evaluating self-monitoring blood glucose strategies using a diabetic-patient software agent. Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on, IEEE.
WHO. 2015. http://www.who.int/topics/diabetes_mellitus/en/. Retrieved October, 2015.