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

Evrim Özmen; Büşra Emir

Makale detayları

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

Yazarlar:

Evrim Özmen | Büşra Emir

Yayıncı:

Eskişehir Osmangazi University

Ana Başlıklar:

Epidemiyoloji (Diğer)

Dergi:

Osmangazi Tıp Dergisi Vol.46 Issue 6, pp.878-888

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323752

pISSN:

1305-4953

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Evrim Özmen | Büşra Emir

Yayıncı:

Eskişehir Osmangazi University

Ana Başlıklar:

Epidemiyoloji (Diğer)

Dergi:

Osmangazi Tıp Dergisi Vol.46 Issue 6, pp.878-888

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323752

pISSN:

1305-4953

sepsis

machine learning

intensive care units

mortality

artificial intelligencesepsis

makine öğrenmesi

yoğun bakım üniteleri

mortalite

yapay zekâ

Sayı

Makale detayları

Yazarlar: Evrim Özmen | Büşra Emir

Yayıncı: Eskişehir Osmangazi University

Ana Başlıklar: Epidemiyoloji (Diğer)

Dergi: Osmangazi Tıp Dergisi Vol.46 Issue 6, pp.878-888

Belge Tipi: Makale

Yayın Tarihi: 7/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001323752

pISSN: 1305-4953

Sayı

Yazarlar:

Evrim Özmen | Büşra Emir

Yayıncı:

Eskişehir Osmangazi University

Ana Başlıklar:

Epidemiyoloji (Diğer)

Dergi:

Osmangazi Tıp Dergisi Vol.46 Issue 6, pp.878-888

Belge Tipi:

Makale

Yayın Tarihi:

7/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001323752

pISSN:

1305-4953