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

Yıl 2024, , 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

Nursing Strategies for Diabetic Patient Management: Predicting Parameter Values Post-Exenatide Treatment with Machine Learning Algorithm

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

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