Makine Öğrenme Tabanlı Hastane Triyaj Karar Destek Sistemi Uygulamasının Geliştirilmesi

Öz

Bu çalışma ile hastanelerde yoğunluğun en fazla olduğu birim olan Acil bölümü için makine öğrenme tabanlı bir karar destek sistemi önerilmiştir. Yoğunluk, iş yükü, stres ve insan kaynaklı etkilerden dolayı çalışma alanının baskılandığı Acil birimi için hata payı azaltılarak gelen hastanın en doğru şekilde tıbbi destek alacağı birime yönlendirilme işlemleri yapılmaktadır. Acil birimlerinde gelen hastanın yeşil, sarı ve kırmızı alan olarak adlandırılan alanlara yönlendirilir. Hastaların doğru ve hızlı bir şekilde yönlendirilmesi bu alanlardaki yoğunluğun en verimli şekilde kullanılmasını ve hastaların tıbbi müdahale önceliklerine göre en doğru şekilde destek almasını sağlamaktadır. Bu çalışmada Kore’de bir bölgesel ve yerel hastanelerin acil bölümlerinden elde edilen açık kaynak veri seti kullanılmıştır. karar ağacı, Gaussian naif bayes, k-en yakın komşu, lightgbm, rasgele orman ve destek vektör makinesi kullanılarak sınıflandırma modelleri eğitilmiştir. Bu sınıflandırma modelleri ve eğitim setindeki verilerin girişinin yapıldığı ve hastanelerdeki acil birimlerinin kullanımına yönelik grafiksel arayüz Java swing kütüphanesi kullanılarak geliştirilmiştir. Sınıf modelleri ise Python ortamında derlenmiştir. Java ve Python komut satırı üzerinden haberleştirilerek uygulama bütün halinde çalıştırılarak gerçek ortam testlerine uygun hale getirilmiştir. Hastaların yeşil (under triage), sarı (normal triage) ve yeşil (over triage) alanlara doğru olarak yönlendirilmesinde geliştirilen makine öğrenme tabanlı karar destek sisteminde rasgele orman ağacı ve LGBM %96.1’luk test doğruluğuna ulaşmıştır.

Anahtar Kelimeler

• Triyaj
• karar destek sistemi
• makine öğrenmesi
• grafiksel arayüz

Destekleyen Kurum

Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Proje Numarası

124B804

Development of Machine Learning Based Hospital Triage Decision Support System Application

Öz

This study proposes a machine learning-based decision support system for the emergency department (ED), which is typically the most congested unit in hospitals. In EDs, where the workspace is highly constrained by congestion, workload, stress, and human factors, the margin of error is significantly reduced. Therefore, it is crucial to ensure that incoming patients are directed to the most appropriate unit for medical support. In emergency departments, incoming patients are directed to areas designated as Green, Yellow, and Red Zones. Accurate and rapid patient orientation is essential for the most efficient use of capacity in these congested areas and ensures that patients receive the most appropriate support according to their medical priorities. This study utilizes an open-source dataset obtained from the emergency departments of regional and local hospitals in Korea. Classification models were trained using Decision Trees, Gaussian Naive Bayes, K-Nearest Neighbors, LightGBM, Random Forests, and Support Vector Machines. These classification models, along with the training data, were developed with the Java Swing library to create a graphical interface for use in hospital emergency departments. The classification models themselves were compiled in Python. The entire application was run via the Java and Python command line, making it suitable for real-world testing. The Random Forest and LGBM models achieved %96.1 test accuracy in the machine learning-based decision support system developed to correctly direct patients to the Green (under triage), Yellow (normal triage), and Red (over triage) areas.

Anahtar Kelimeler

• Triage
• decision support system
• machine learning
• graphical interface

Destekleyen Kurum

The Scientific and Technological Research Council of Türkiye

Proje Numarası

124B804

Kaynakça

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