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Diyabetik Hasta Yönetiminde Hemşirelik Stratejileri: Eksenatid Tedavisi Sonrası Parametrelerin Değerlerinin Makine Öğrenme Algoritmasıyla Tahmin Edilmesi

Yıl 2024, Cilt: 15 Sayı: 1, 92 - 105, 22.04.2024

Sıddıka Ersoy Remzi Gürfidan

https://doi.org/10.22312/sdusbed.1449989

Öz

Diabetes Mellitus'un (DM) küresel ölçekteki artışı obezite oranlarındaki artışa paraleldir; Türkiye'de yetişkinler arasında diyabet prevalansı %13,7, obezite ise %32'dir. Diyabet ve obezitenin iç içe geçmiş doğası ve ilave kronik hastalık riskinin artması nedeniyle diyabet hastalarının yönetimi kapsamlı bir yaklaşımı gerektirmektedir. Diyabet hemşireleri, düzenli değerlendirmeler, kan şekeri takibi, ilaç yönetimi ve hasta eğitimini kapsayan diyabet bakımında çok önemli bir rol oynamaktadır. İnkretin-mimetik glukagon benzeri peptid-1 reseptör agonistleri (GLP-1A), diyabet ve kilo kontrolünde üstünlük göstererek bunları ikinci basamak tedaviler olarak konumlandırmıştır. Diyabet hemşirelerinin diyabet hastalarına diyet rehberliği, fiziksel aktivite teşviki ve kilo verme yardımı yoluyla hayati destek sağlamasıyla, kilo yönetimi diyabet bakımında temel olmaya devam etmektedir. GLP-1A tedavisine hasta yanıtlarını tahmin etmek, tedavi sonuçlarını optimize etmek, kararları kolaylaştırmak ve potansiyel komplikasyonları önlemek için çok önemlidir.
Yapay zeka (AI) ve makine öğrenimi (ML), sağlık hizmeti sunumunu geliştirmek için umut verici yollar sunmaktadır. Çalışma eksenatid kullanan diyabet hastalarında makine öğrenmesi algoritmalarını kullanarak açlık kan şekeri düzeylerini, HbA1C değerlerini ve kilo kaybı sonuçlarını tahmin etmeyi amaçlamaktadır. Batı Akdeniz'deki gerçek hasta verilerinin analiz edildiği bu çalışma, kilo kaybı, açlık kan şekeri düzeyleri ve HbA1C değerlerini tahmin etmede SVR algoritması sırasıyla %99.9, %99.9 ve %97.3'lük başarı oranlarına ulaşmıştır.
Bulgularımız hemşirelikte, özellikle de diyabetik hasta yönetimine yönelik prognostik modellemede yapay zeka odaklı yaklaşımların potansiyelinin altını çizmektedir. Hemşireler, makine öğreniminden yararlanarak tedavi yanıtlarını tahmin edebilir, karar alma sürecini kolaylaştırabilir ve hasta bakım kalitesini yükseltebilir. Yapay zeka uygulamaları geliştikçe, bu teknolojileri hemşirelik rollerine entegre etmek, hasta merkezli bakımı ilerletmeyi ve sağlık sonuçlarını optimize etmeyi vaat etmektedir.
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Anahtar Kelimeler

Diyabet yönetimi inkretin tedavi diyabet hemşireliği makine öğrenmesi algoritmaları

Kaynakça

  • [1] TEMD: Türkiye Endokrinoloji ve Metabolizma Dernegi, “, Diabetes Mellitus ve Komplikasyonlarının Tanı, Tedavi ve Izlem Kılavuzu-2019,” Miki Matbaacılık San. ve Tic. Ltd, vol. 12, no. 1, 2019.
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Nursing Strategies for Diabetic Patient Management: Predicting Parameter Values Post-Exenatide Treatment with Machine Learning Algorithm

Yıl 2024, Cilt: 15 Sayı: 1, 92 - 105, 22.04.2024

Sıddıka Ersoy Remzi Gürfidan

https://doi.org/10.22312/sdusbed.1449989

Öz

The global escalation of DM parallels the rise in obesity rates, with Turkey experiencing a prevalence of 13.7% for diabetes and 32% for obesity among adults. Managing diabetic patients necessitates a comprehensive approach due to the intertwined nature of diabetes and obesity, along with the heightened risk of additional chronic illnesses. Diabet nurses play a pivotal role in diabetic care, encompassing regular assessments, blood glucose monitoring, medication management, patient education. Incretin-mimetic glucagon-like peptide-1 receptor-agonists (GLP-1A) have demonstrated superiority in diabetes, weight control, positioning them as second-line treatments. Weight management remains fundamental in diabetes care, with Diabet nurses providing vital support through dietary guidance, physical activity promotion, and weight loss assistance for diabetic patients. Predicting patient responses to GLP-1A therapy is crucial for optimizing treatment outcomes, streamlining decisions, averting potential complications.

Artificial intelligence (AI) and machine learning (ML) offer promising avenues for enhancing healthcare delivery. Our study aimed to forecast fasting blood sugar levels, HbA1C values, and weight loss outcomes in diabetic patients using exenatide, utilizing the random forest algorithm. Analyzing real patient data from the Western-Mediterranean, this study achieved substantial success rates of %99.9, %99.9 and %97.3 in predicting weight loss, fasting blood sugar levels, and HbA1C values, respectively.

Our findings underscore the potential of AI-driven approaches in nursing, particularly in prognostic modeling for diabetic patient management. By leveraging ML, nurses can anticipate treatment responses, streamline decision-making, and elevate patient care quality. As AI applications evolve, integrating these technologies into nursing roles promises to advance patient-centered care and optimize health outcomes.

Anahtar Kelimeler

Diabetes management incretin therapy diabet nursing machine learning algorithm

Kaynakça

  • [1] TEMD: Türkiye Endokrinoloji ve Metabolizma Dernegi, “, Diabetes Mellitus ve Komplikasyonlarının Tanı, Tedavi ve Izlem Kılavuzu-2019,” Miki Matbaacılık San. ve Tic. Ltd, vol. 12, no. 1, 2019.
  • [2] T. H. S. K. T.C. Sağlık Bakanlığı, “TC. Sağlık Bakanlığı: Türkiye Diyabet Programı 2015-20,” Ankara, 2014.
  • [3] IDF: Internatıonal Dıabetes Federation., “ Diabetes Atlas 2013,” 2013.
  • [4] A. H. Mokdad et al., “Prevalence of Obesity, Diabetes, and Obesity-Related Health Risk Factors, 2001,” JAMA, vol. 289, no. 1, pp. 76–79, Jan. 2003, doi: 10.1001/JAMA.289.1.76.
  • [5] H. E. Bays, R. H. Chapman, and S. Grandy, “The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys,” Int J Clin Pract, vol. 61, no. 5, pp. 737–747, May 2007, doi: 10.1111/J.1742-1241.2007.01336.X.
  • [6] I. Satman et al., “Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish adults,” Eur J Epidemiol, vol. 28, no. 2, pp. 169–180, Feb. 2013, doi: 10.1007/S10654-013-9771-5/TABLES/2.
  • [7] A. B. Evert et al., “Nutrition therapy for adults with diabetes or prediabetes: A consensus report,” Diabetes Care, vol. 42, no. 5, pp. 731–754, 2019, doi: 10.2337/DCI19-0014/-/DC1.
  • [8] M. A. Powers et al., “Diabetes Self-management Education and Support in Type 2 Diabetes: A Joint Position Statement of the American Diabetes Association, the American Association of Diabetes Educators, and the Academy of Nutrition and Dietetics,” Diabetes Educator, vol. 43, no. 1, pp. 40–53, Feb. 2017, doi: 10.1177/0145721716689694/ASSET/IMAGES/LARGE/ 10.1177_0145721716689694-FIG2.JPEG.
  • [9] E. L. Johnson et al., “Standards of Medical Care in Diabetes—2019 Abridged for Primary Care Providers,” Clin Diabetes, vol. 37, no. 1, p. 11, Jan. 2019, doi: 10.2337/CD18-0105.
  • [10] A. D. Association, “6. Glycemic Targets: Standards of Medical Care in Diabetes—2018,” Diabetes Care, vol. 41, no. Supplement_1, pp. S55–S64, Jan. 2018, doi: 10.2337/DC18-S006.
  • [11] M. Lambert, “ADA Releases Revisions to Recommendations for Standards of Medical Care in Diabetes,” Am Fam Physician, vol. 85, no. 5, pp. 514–515, Mar. 2012, Accessed: Mar. 01, 2024. [Online]. Available: https://www.aafp.org/pubs/afp/issues/2012/0301/p514.html
  • [12] H. Elrıck, L. Stımmler, C. J. Hlad, and Y. Araı, “Plasma Insulin Response to Oral and Intravenous Glucose Administration,” J Clin Endocrinol Metab, vol. 24, no. 10, pp. 1076–1082, Oct. 1964, doi: 10.1210/JCEM-24-10-1076.
  • [13] R. Çolak, “Tip 2 diabetes mellitus tedavisinde inkretinler,” J Exp Clin Med, vol. 29, no. 1s, pp. 30–38, Jun. 2012, doi: 10.5835/JECM.OMU.29.S1.007.
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  • [19] T. L. A. R. Group, “Cardiovascular Effects of Intensive Lifestyle Intervention in Type 2 Diabetes,” https://doi.org/10.1056/NEJMoa1212914, vol. 369, no. 2, pp. 145–154, Jul. 2013, doi: 10.1056/NEJMOA1212914.
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  • [24] M. Long, Y. Cao, Z. Cao, J. Wang, and M. I. Jordan, “Transferable Representation Learning with Deep Adaptation Networks,” IEEE Trans Pattern Anal Mach Intell, vol. 41, no. 12, pp. 3071–3085, Dec. 2019, doi: 10.1109/TPAMI.2018.2868685.
  • [25] S. Spanig, A. Emberger-Klein, J. P. Sowa, A. Canbay, K. Menrad, and D. Heider, “The virtual doctor: An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes,” Artif Intell Med, vol. 100, p. 101706, Sep. 2019, doi: 10.1016/J.ARTMED.2019.101706.
  • [26] J. Baumbach and H. H. H. W. Schmidt, “The End of Medicine as We Know It: Introduction to the New Journal, Systems Medicine,” https://home.liebertpub.com/sysm, vol. 1, no. 1, pp. 1–2, Feb. 2018, doi: 10.1089/SYSM.2017.28999.JBA.
  • [27] A. Talaei-Khoei and J. M. Wilson, “Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables,” Int J Med Inform, vol. 119, pp. 22–38, Nov. 2018, doi: 10.1016/J.IJMEDINF.2018.08.008.
  • [28] E. Rosten and T. Drummond, “Machine Learning for High-Speed Corner Detection,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3951 LNCS, pp. 430–443, 2006, doi: 10.1007/11744023_34.
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Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Dahili Hastalıklar Hemşireliği
Bölüm Araştırma Makaleleri
Yazarlar

Sıddıka Ersoy 0000-0001-8094-8042

Remzi Gürfidan 0000-0002-4899-2219

Yayımlanma Tarihi 22 Nisan 2024
Gönderilme Tarihi 9 Mart 2024
Kabul Tarihi 15 Nisan 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 15 Sayı: 1

Kaynak Göster

Vancouver Ersoy S, Gürfidan R. Nursing Strategies for Diabetic Patient Management: Predicting Parameter Values Post-Exenatide Treatment with Machine Learning Algorithm. Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi. 2024;15(1):92-105.
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