首页|Naval Medical University Reports Findings in Machine Learning (Enhancing Perform ance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma)
Naval Medical University Reports Findings in Machine Learning (Enhancing Perform ance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Shanghai, Peop le’s Republic of China, by NewsRx journalists, research stated, “Prehospital tra uma triage is essential to get the right patient to the right hospital. However, the national field triage guidelines proposed by the American College of Surgeo ns have proven to be relatively insensitive when identifying severe traumas.” The news reporters obtained a quote from the research from Naval Medical Univers ity, “This study aimed to build a prehospital triage model to predict severe tra uma and enhance the performance of the national field triage guidelines. This wa s a multisite prediction study, and the data were extracted from the National Tr auma Data Bank between 2017 and 2019. All patients with injury, aged 16 years of age or older, and transported by ambulance from the injury scene to any trauma center were potentially eligible. The data were divided into training, internal, and external validation sets of 672,309; 288,134; and 508,703 patients, respect ively. As the national field triage guidelines recommended, age, 7 vital signs, and 8 injury patterns at the prehospital stage were included as candidate variab les for model development. Outcomes were severe trauma with an Injured Severity Score 16 (primary) and critical resource use within 24 hours of emergency depart ment arrival (secondary). The triage model was developed using an extreme gradie nt boosting model and Shapley additive explanation analysis. The model’s accurac y regarding discrimination, calibration, and clinical benefit was assessed. At a fixed specificity of 0.5, the model showed a sensitivity of 0.799 (95% CI 0.797-0.801), an undertriage rate of 0.080 (95% CI 0.079-0.081) , and an overtriage rate of 0.743 (95% CI 0.742-0.743) for predict ing severe trauma. The model showed a sensitivity of 0.774 (95 % CI 0.772-0.776), an undertriage rate of 0.158 (95% CI 0.157-0.159), and an overtriage rate of 0.609 (95% CI 0.608-0.609) when predicti ng critical resource use, fixed at 0.5 specificity. The triage model’s areas und er the curve were 0.755 (95% CI 0.753-0.757) for severe trauma pre diction and 0.736 (95% CI 0.734-0.737) for critical resource use p rediction. The triage model’s performance was better than those of the Glasgow C oma Score, Prehospital Index, revised trauma score, and the 2011 national field triage guidelines RED criteria. The model’s performance was consistent in the 2 validation sets. The prehospital triage model is promising for predicting severe trauma and achieving an undertriage rate of <10% .”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
