An intelligent auxiliary diagnostic system based on 11 clinical parameters achieved an average accuracy of 0.89 in internal tests and 0.84 in external validation for rhabdomyolysis.
A machine learning-based auxiliary diagnostic system using 11 clinical indicators demonstrated high accuracy in predicting acute kidney injury and death risk in patients with rhabdomyolysis.
Introduction: Crush syndrome, also known as traumatic rhabdomyolysis, often occurs in disasters like earthquakes, traffic accidents, and building collapses. It results in muscle cell necrosis, leading to symptoms such as hypovolemic shock, hyperkalemia, and acute kidney injury (AKI). The mortality rate of rhabdomyolysis is about 10%. An intelligent auxiliary diagnostic system for rhabdomyolysis based on machine learning is crucial. Methods: This study aimed to develop an intelligent auxiliary diagnostic system, utilizing the MIMIC-III database and electronic medical records from several Chinese hospitals. The system was trained and validated with 70% and 30% of patient data, respectively. A variety of machine learning methods were used to establish injury grading and prognosis assessment models, and statistical methods and feature importance assessment methods were used to analyze and explain the features selected by the model. Results: The 22 clinical indicators originally included in the modeling were reduced to 11 through feature analysis and feature screening. In internal tests, the auxiliary diagnostic system based on 11 parameters achieved an average accuracy of 0.89. External validation with 360 cases showed an average accuracy of 0.84, with an AKI prediction accuracy of 0.83 and an AUC value of 0.82. Death risk prediction accuracy reached 0.89. Conclusion: This intelligent auxiliary diagnostic system provides valuable recommendations for the diagnosis and treatment of rhabdomyolysis, improving treatment success rates and optimizing medical resource allocation in disaster situations. By leveraging machine learning, we have established a robust and reliable tool to assist healthcare providers in managing this complex condition.
Lv et al. (2026) studied Rhabdomyolysis. Intelligent auxiliary diagnostic system based on machine learning was evaluated on Diagnostic and prognostic accuracy. An intelligent auxiliary diagnostic system based on 11 clinical parameters achieved an average accuracy of 0.89 in internal tests and 0.84 in external validation for rhabdomyolysis.