Predicting triage levels in patients presenting with cardiac-related symptoms: a comparison of supervised machine
Amirhossein Yazdi1, Mohadeseh Noori1, Seyed Mohammad Ayyoubzadeh2
1Department of Cardiology, School of Medicine, Clinical Research Development Unit of Farshchian Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
Insights
Machine learning accurately predicts triage levels for cardiac patients, with Random Forest showing the best performance. This aids in identifying high-risk individuals and optimizing resource allocation in emergency departments.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Accurate patient triage in emergency departments is crucial for resource allocation, especially for cardiac patients.
- Identifying high-risk patients promptly ensures timely intervention and efficient healthcare delivery.
Purpose of the Study:
- To predict the triage level of patients with cardiac symptoms using machine learning.
- To compare the performance of various machine learning algorithms for cardiac patient triage.
Main Methods:
- A literature review and expert survey identified key factors influencing triage.
- Patient data from 1862 individuals were collected from a cardiac hospital's triage unit.
- Five machine learning models (Random Forest, Logistic Regression, SVM, KNN, GB) were applied for analysis.
Main Results:
- Random Forest achieved the highest accuracy (93.57%), Cohen's Kappa (0.82), and F1-score (0.93).
- Gradient Boosting and SVM also demonstrated strong performance.
- Key factors influencing triage included high-risk conditions, need for life-saving intervention, chief complaint, and consciousness level.
Conclusions:
- Machine learning models effectively discriminate between low-risk and high-risk cardiac patients.
- The Random Forest model offers superior performance for cardiac patient triage.
- These AI-driven tools can enhance emergency department resource allocation for critical cardiac cases.
Introduction:
Determining the triage level of patients upon their arrival at the hospital emergency department is highly important for identifying high-risk patients and allocating resources to them. This issue can be of even greater importance in patients presenting with cardiac-related symptoms. This study was conducted to predict the triage level of patients presenting with cardiac-related symptoms using machine learning methods and to compare the performance of different approaches.
Methods:
This prospective study was conducted in 2024 in three main steps. In the first step, a literature review was performed, and the factors influencing patient triage levels were extracted from previous studies. Then the identified factors from the literature review were presented to experts for their opinion, and the final influential factors were determined based on their feedback. In the second step, patient data were collected from the triage unit of a specialized cardiac hospital. In the third and final step, the collected data were preprocessed and then analyzed using Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB) machine learning methods.
Results:
After reviewing the literature and surveying experts, the confirmed factors were finally identified, and data related to 1862 patients were collected. 52% of the participants in this study were male. RF achieved the highest performance with a test accuracy of 93.57%, Cohen's Kappa of 0.82, and a weighted F1-score of 0.93, followed by GB (accuracy = 90.08%, Kappa = 0.73) and SVM (accuracy = 86.6%, Kappa = 0.66). The most influential factors on patients' triage levels included: the type of high-risk condition that elevates a patient to Level 2, need for life-saving intervention, having high-risk conditions (what condition), chief complaint, level of consciousness, and diseases.
Conclusion:
In this study, five machine learning models were utilized for the triage of patients presenting with cardiac-related symptoms. The results of the study indicated that these algorithms had a good ability to discriminate between patients with different triage levels. The Random Forest method performed slightly better than the other techniques. These techniques can be used to differentiate between low-risk and high-risk patients and to allocate resources to high-risk patients.
Related Concept Videos
01:30Heart Failure IV: Classification and Diagnostic Evaluation
01:29Cardiomyopathy III: Hypertrophic Cardiomyopathy