Prediction of The Patients at Risk for Development Hematoma After Percutaneous Coronary Angiography: A Nursing Decision Support Model Pilot Study

İlknur Buçan Kıkrbir; Yeter Kurt

Makale detayları

Prediction of The Patients at Risk for Development Hematoma After Percutaneous Coronary Angiography: A Nursing Decision Support Model Pilot Study

Yazarlar:

İlknur Buçan Kıkrbir | Yeter Kurt

Yayıncı:

Gumushane University

Ana Başlıklar:

Hemşirelik (Diğer)

Dergi:

Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi Vol.13 Issue 2, pp.571-578

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320958

pISSN:

2146-9954

Anahtar kelimeler:

Özet:

 The study aimed to develop a nursing clinical decision support model using the machine learning method, which is one of the important fields today, to identify patients with risk of hematoma development after Percutaneous Coronary Intervention and to help plan appropriate nursing interventions. In this study, the data of 100 patients with myocardial infarction was used in the development of the decision support model. R open-source programming language was used for statistical analysis of the data and the random forest method, one of the machine learning methods was used for the development of the model. The result of this pilot study, a nursing decision support model with a sensitivity of 69 and a specificity of 64 was developed with the Random forest method using 24 features regarding the demographic, laboratory, and percutaneous coronary intervention procedures of the patients.

 The study aimed to develop a nursing clinical decision support model using the machine learning method, which is one of the important fields today, to identify patients with risk of hematoma development after Percutaneous Coronary Intervention and to help plan appropriate nursing interventions. In this study, the data of 100 patients with myocardial infarction was used in the development of the decision support model. R open-source programming language was used for statistical analysis of the data and the random forest method, one of the machine learning methods was used for the development of the model. The result of this pilot study, a nursing decision support model with a sensitivity of 69 and a specificity of 64 was developed with the Random forest method using 24 features regarding the demographic, laboratory, and percutaneous coronary intervention procedures of the patients.

 The study aimed to develop a nursing clinical decision support model using the machine learning method, which is one of the important fields today, to identify patients with risk of hematoma development after Percutaneous Coronary Intervention and to help plan appropriate nursing interventions. In this study, the data of 100 patients with myocardial infarction was used in the development of the decision support model. R open-source programming language was used for statistical analysis of the data and the random forest method, one of the machine learning methods was used for the development of the model. The result of this pilot study, a nursing decision support model with a sensitivity of 69 and a specificity of 64 was developed with the Random forest method using 24 features regarding the demographic, laboratory, and percutaneous coronary intervention procedures of the patients.

 Çalışmada, günümüzün önemli alanlarından biri olan makine öğrenmesi yöntemini kullanarak, Perkütan Koroner Girişim sonrası hematom gelişme riski taşıyan hastaların belirlenmesi ve uygun hemşirelik girişimlerinin planlanmasına yardımcı olacak bir hemşirelik klinik karar destek modelinin geliştirilmesi amaçlandı. Bu çalışmada karar destek modelinin geliştirilmesinde 100 miyokard enfarktüsü hastasının verileri kullanıldı. Verilerin istatistiksel analizinde R açık kaynak programlama dili kullanılmış olup, modelin geliştirilmesinde makine öğrenmesi yöntemlerinden biri olan rastgele orman yöntemi kullanılmıştır. Bu pilot çalışmanın sonucunda hastaların demografik, laboratuvar ve perkütan koroner girişim işlemlerine ilişkin 24 özelliği kullanarak Rastgele orman yöntemiyle 69 duyarlılığa ve 64 özgüllüğe sahip bir hemşirelik karar destek modeli geliştirildi.

Makale detayları

Prediction of The Patients at Risk for Development Hematoma After Percutaneous Coronary Angiography: A Nursing Decision Support Model Pilot Study

Yazarlar:

İlknur Buçan Kıkrbir | Yeter Kurt

Yayıncı:

Gumushane University

Ana Başlıklar:

Hemşirelik (Diğer)

Dergi:

Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi Vol.13 Issue 2, pp.571-578

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320958

pISSN:

2146-9954

Coronary angiography

decision support systems

machine learning

nursing informatics

artificial intelligenceHemşirelik bilişimi

karar destek sistemi

koroner anjiyografi

makine öğrenmesi

yapay zeka

Sayı

Makale detayları

Yazarlar: İlknur Buçan Kıkrbir | Yeter Kurt

Yayıncı: Gumushane University

Ana Başlıklar: Hemşirelik (Diğer)

Dergi: Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi Vol.13 Issue 2, pp.571-578

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001320958

pISSN: 2146-9954

Sayı

Yazarlar:

İlknur Buçan Kıkrbir | Yeter Kurt

Yayıncı:

Gumushane University

Ana Başlıklar:

Hemşirelik (Diğer)

Dergi:

Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi Vol.13 Issue 2, pp.571-578

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001320958

pISSN:

2146-9954