Sepsis Teşhisinde Makine Öğrenmesi Algoritmalarının Rolü: Bibliyometrik Analiz Kullanarak Retrospektif Genel Bakış

Öz

Makine öğrenmesi, büyük veri setlerinden anlamlı bilgiler çıkarma ve hastalık teşhisi için güçlü tahmin modelleri oluşturma konusunda büyük bir potansiyele sahiptir. Bu çalışmanın amacı, makine öğrenmesi algoritmalarının sepsis teşhisindeki rolüne ilişkin kapsamlı bir incelemesini gerçekleştirmektir. Araştırma bibliyometrik analiz yöntemi ile gerçekleştirilmiştir. Araştırma kapsamında Web of Science (WoS) Core Collection veri tabanında gelişmiş arama sorgusu oluşturularak 2000-2024 yılları arasında yayınlanan WoS dizini Science Citation Index Expanded (SCI-Exp), yayın türü makale, yayın dili ingilizce, açık erişimli yayınlar dahil edildi. WoS veri tabanında 05.07.2024 tarihinde ilgili anahtar kelimelerle oluşturulan gelişmiş arama sorgusu kullanılarak 277 yayına ulaşıldı. Sepsis ve makine öğrenmesini içermeyen, ingilizce olmayan 87 yayın dışlanarak 190 yayının analizi yapılmıştır. Bibliyometrik analiz sonucunda elde edilen kelime haritasında ilk beş anahtar kelime sırasıyla sepsis, makine öğrenmesi, yoğun bakım üniteleri, mortalite ve yapay zekâ yer almaktadır. En çok yayına sahip olan ülke Çin, en çok atıf alan ülke Amerika iken, dergiler arasında en çok makaleye sahip olan “Frontiers in Medicine”, en çok atıf alan yayının olduğu dergi “Critical Care Medicine” oldu. 2000-2024 yılları arasında yayınlanan makalelerin analizine göre, sepsis teşhisinde yapay zekâ ve makine öğrenmesi kullanımı, özellikle yoğun bakım ünitelerinde önemli bir potansiyele sahiptir. Bu teknolojilerin erken teşhis, hastalık sınıflandırması ve prognoz tahmininde etkili bir şekilde kullanılabileceğini ortaya koymaktadır. Araştırma iş birliği ağlarının yoğunlaşması ve belirlenen anahtar kelimeler etrafında yoğunlaşan yayınların artması, bu alandaki araştırma eğilimlerinin gelecekte daha da büyüyeceğini işaret etmektedir.

Anahtar Kelimeler

• sepsis
• makine öğrenmesi
• yoğun bakım üniteleri
• mortalite
• yapay zekâ

Destekleyen Kurum

İzmir Kâtip Çelebi Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Proje Numarası

2024-TYL-SABE-0011

Etik Beyan

Bu çalışma WoS veritabanından elde edilen elektronik bibliyometrik veriler üzerinde yürütüldüğünden ve herhangi bir hasta verisi içermediğinden etik komite veya kurumsal inceleme onayı gerektirmemektedir.

Teşekkür

Bu çalışma, İzmir Katip Çelebi Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü Birimi'nin 2024-TYL-SABE-0011 numaralı proje hibe desteğiyle desteklenmiştir. Bu çalışmadan kısa bir kesit, 30 Temmuz 2024'te Türkiye, İzmir'de düzenlenen 1. Uluslararası Veri Analitiği Kongresi'nde sözlü sunum olarak sunulmuştur.

The Role of Machine Learning Algorithms in Sepsis Diagnosis: A Retrospective Overview using Bibliometric Analysis

Öz

Machine learning has great potential to extract meaningful information from large data sets and build powerful predictive models for disease diagnosis. The aim of this study is to conduct a comprehensive review of the role of machine learning algorithms in sepsis diagnosis. The research was conducted using the bibliometric analysis method. Within the scope of the research, an advanced search query was created in the Web of Science (WoS) Core Collection database and WoS index Science Citation Index Expanded (SCI-Exp), publication type article, publication language English, open access publications published between 2000 and 2024 were included. In the WoS database, 277 publications were accessed using an advanced search query created with the relevant keywords on 05.07.2024. After excluding 87 non-English publications that did not include sepsis and machine learning, 190 publications were analyzed. In the treemap obtained in bibliometric analysis, the first five keywords include sepsis, machine learning, intensive care units, mortality, and artificial intelligence, respectively. China led in publication count, whereas the USA boasted the most cited publications. "Frontiers in Medicine" featured the highest number of articles, while "Critical Care Medicine" contained the most cited ones. According to the analysis of articles published, the use of artificial intelligence and machine learning in sepsis diagnosis has significant potential, especially in intensive care units. These technologies show promise in early diagnosis, disease classification, and prognosis prediction. Expanding research collaborations and a growing publication focus on key themes suggest continued growth in this research area.

Anahtar Kelimeler

• sepsis
• machine learning
• intensive care units
• mortality
• artificial intelligence

Destekleyen Kurum

Izmir Katip Celebi University Scientific Research Projects Coordination Unit

Proje Numarası

2024-TYL-SABE-0011

Etik Beyan

Since this study was conducted on electronic bibliometric data obtained from the WoS database and did not include any patient data, an ethics committee or institutional review approval was not required

Teşekkür

This study has been supported by the grant of Izmir Katip Celebi University Scientific Research Projects Coordination Unit, Project No: 2024-TYL-SABE-0011. A brief excerpt from this study was presented as an oral presentation at the 1st International Data Analytics Congress on July 30, 2024, in Izmir, Turkey.

Kaynakça

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