Medical Informatics IV

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DOI :10.26650/B/T3.2024.40.005   IUP :10.26650/B/T3.2024.40.005    Full Text (PDF)

Detection of Biomarkers Affecting Mortality: An Emergency Department Study

Büşra Çelik, M. Fevzi Esen, Ahmet Fatih Deveci

In this study, data mining techniques were employed to evaluate a dataset of 1106 patients between 15-84 ages who were admitted to the emergency department of a hospital, between February 1, 2021 - May 1, 2021. All patients experienced mortality within 24 hours or after 24 hours following the admission of the emergency department. The aim was to identify significant biomarkers affecting mortality. Accordingly, the focus was on various tests providing essential information about the patient’s metabolic functions and general health status, such as Be, Hbg, HCO3, Htc, Inr, Lac, Mean Arterial Pressure (MAP), pCO2, pH, heart rate, and pulse pressure. Furthermore, variables related to the Injury Severity Score (ISS) used for classifying injuries in trauma patients and the Glasgow Coma Scale (GCS) to assess the overall level of consciousness were included into the dataset. Supervised machine learning algorithms, including Support Vector Machines (SVM), Decision Trees (DT), Naive Bayes (NB), Logistic Regression (LR), and Multi-Layer Perceptrons (MLP) were performed to classify cases. As a result, the highest accuracy rate for mortality prediction was achieved with SVM. MAP, Lac, ISS, and GCS were determined to be the significant variables for separating the classes.


Keywords: Data mining, machine learning, mortality, emergency medicine
DOI :10.26650/B/T3.2024.40.005   IUP :10.26650/B/T3.2024.40.005    Full Text (PDF)

Mortali̇teyi̇ Etki̇leyen Bi̇yobeli̇rteçleri̇n Tespi̇ti̇: Bi̇r Aci̇l Servi̇s Çalışması

Büşra Çelik, M. Fevzi Esen, Ahmet Fatih Deveci

Bu çalışmada, 1 Şubat 2021 - 1 Mayıs 2021 tarihleri arasında bir hastanenin acil servisine başvuran 15-84 yaş arası 1106 hastadan oluşan bir veri kümesini veri madenciliği teknikleriyle analiz edilerek, mortaliteye etki eden önemli değişkenlerin belirlenmesi amaçlanmıştır. Bu doğrultuda, Be, Hbg, Hco3, Htc, Inr, Lac, Ortalama Arter Basıncı (MAP), PCO2, pH, kalp hızı ve nabız basıncı gibi hastanın metabolik işlevleri ve genel sağlık durumu hakkındaki çeşitli değişkenler ele alınmıştır. Ayrıca, travma sınıflandırılmasında kullanılan Yaralanma Şiddeti Skoru (ISS) ve genel bilinç düzeyini değerlendirmek için kullanılan Glasgow Koma Skalası (GCS) veri setine dahil edilmiştir. Vakaları sınıflandırmak için Destek Vektör Makineleri (SVM), Karar Ağaçları (DT), Naive Bayes (NB), Lojistik Regresyon (LR) ve Çok Katmanlı Algılayıcılar (MLP) dahil olmak üzere denetimli makine öğrenimi algoritmaları kullanılmıştır. Sonuç olarak, mortalite tahmini için en yüksek doğruluk oranının SVM ile elde edildiği tespit edilmiş olup, MAP, Lac, ISS ve GCS değişkenlerinin sınıfları ayırmada kullanılabilecek önemli değişkenler olduğu belirlenmiştir.


Keywords: Veri madenciliği, makine öğrenmesi, mortalite, acil tıp

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