학술논문
A new machine learning technique for predicting traumatic injuries outcomes based on the vital signs
Document Type
Conference
Author
Source
2019 25th International Conference on Automation and Computing (ICAC) Automation and Computing (ICAC), 2019 25th International Conference on. :1-5 Sep, 2019
Subject
Language
Abstract
Traditional vital signs are an essential part of triage assessment in emergency departments (ED), and have been widely used in trauma prediction models. Previous researchers have studied the effect of vital signs scores on predicting traumatic injury outcomes and have found it to be significant. Based on the vital signs’ scores, an Interpretable Machine Learning (IML) method is proposed to predict patient outcomes and is compared with various ML algorithms. Results indicate that the IML method has a comparable performance with a mean AUC of 0.683, and its interpretability would help in the early identification of trauma patients at risk of mortality.