Malnütrisyon durumunun saptanmasında makine öğrenmesinin kullanılması

Abstract

Bireyin beslenme durumu, vücut kompozisyonu ve fonksiyonel durumunun bir belirleyicisidir. Yetersiz beslenme yaşam kalitesini düşürür, hasta sonuçlarını, mortalite ve morbidite riskini artırır, hastanede kalış süresini ve maliyetleri olumsuz etkiler. Malnütrisyon, enerji, protein ve diğer besin öğelerinin eksikliğinin veya fazlalığının (veya dengesizliğinin) doku/vücut formu (vücut şekli, boyutu ve bileşimi) ve işlevi ile klinik sonuçlar üzerinde ölçülebilir olumsuz etkilere neden olduğu bir beslenme durumudur. Malnütrisyonun erken tanısı için malnütrisyon tarama ve tanı araçlarının geliştirilmesi, hastaların sağlığı, refahı ve uzun vadeli komplikasyonları önlemek için gereklidir. Hastane ortamında kullanılabilecek pek çok beslenme tarama aracı bulunmasına rağmen, en iyi aracın hangisi olduğu konusunda bir fikir birliği bulunmamakta ve tarama uygulamalarına yeterince uyulmadığı için etkin beslenme tedavisine ulaşılamamaktadır. Son yıllarda, makine öğrenimi yöntemleri, klinikte karar vermeye yardımcı olmak ve tedavinin kalitesini, etkinliğini iyileştirmek için birçok tıbbi alanda yaygın olarak uygulanmaktadır. Bu derlemede Pubmed, Google Scholar, Web of Science veri tabanlarında yetersiz beslenme, malnütrisyon, makine öğrenmesi, yapay zeka anahtar kelimeleri ile tarama yapılmıştır ve makine öğrenme yöntemlerinin malnütrisyon tanısında kullanımı incelenmiştir.

Keywords

• algoritma
• makine öğrenmesi
• malnütrisyon
• yetersiz beslenme
• yapay zeka

Supporting Institution

Bu çalışma herhangi bir kurum tarafından desteklenmemiştir

Ethical Statement

Bu çalışmada etik kurul onayına ihtiyaç duyulmamıştır.

The Use of Machine Learning to Assess Malnutrition Status

Abstract

An individual's nutritional status is a determinant of body composition and functional status. Undernourishment reduces quality of life, increases patient outcomes, mortality and morbidity risk, and adversely affects length of hospitalization and costs. Malnutrition is a nutritional state in which a deficiency or excess (or imbalance) of energy, protein and other nutrients causes measurable adverse effects on tissue/body form (body shape, size and composition) and function, and clinical outcomes. The development of malnutrition screening and diagnostic tools for the early detection of malnutrition is essential for the health and well-being of patients and to prevent long-term complications. Although there are many nutritional screening tools that can be used in the hospital setting, there is no consensus on which is the best tool and effective nutritional treatment is not achieved due to poor adherence to screening practices. In recent years, machine learning methods have been widely applied in many medical fields to assist clinical decision-making and improve the quality and effectiveness of treatment. In this review, Pubmed, Google Scholar, Web of Science databases were searched with the keywords undernourishment, malnutrition, machine learning, artificial intelligence and the use of machine learning methods in the diagnosis of malnutrition was examined.

Keywords

• algorithm
• machine learning
• malnutrition
• undernourishment
• artificial intelligence

Supporting Institution

This study was not supported by any organization

Ethical Statement

Ethics committee approval was not required in this study.

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