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

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 t...

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Published inGümüşhane Üniversitesi Sağlık Bilimleri Dergisi Vol. 13; no. 2; pp. 571 - 578
Main Authors Buçan Kıkrbir, İlknur, Kurt, Yeter
Format Journal Article
LanguageTurkish
Published 29.06.2024
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Abstract 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.
AbstractList 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.
Author Kurt, Yeter
Buçan Kıkrbir, İlknur
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