Machine learning models, particularly neural networks, accurately predicted rhabdomyolysis-induced acute kidney injury on the third day after an earthquake, achieving up to 94.44% sensitivity and 99.24% specificity.
Observational (n=1,440)
Yes
Do machine learning models improve the early prediction of rhabdomyolysis-induced acute kidney injury in earthquake victims?
Machine learning models, particularly neural networks, demonstrate high sensitivity and specificity for the early prediction of rhabdomyolysis-induced AKI following catastrophic events, outperforming traditional threshold-based methods.
INTRODUCTION: Rhabdomyolysis-induced acute kidney injury (AKI) is one of the most common complications of catastrophic incidents, especially earthquakes. Early detection of AKI can reduce the burden of the disease. In this paper, data collected from the Bam earthquake was used to find a suitable model that can be used in prediction of AKI in the early stages of the disaster. METHODS: Models used in this paper utilized many inputs, which were extracted from the previously published dataset, but depending on the employed method, other inputs have also been considered. This work has been done in two parts. In the first part, the models were constructed from a smaller set of records, which included all of the required fields and in the second part; the main purpose was to find a way to replace the missing data, as data are mostly incomplete in catastrophic events. The data used belonged to the victims of the Bam earthquake, who were admitted to different hospitals. These data were collected on the first day of the incident via questionnaires that were provided by the Iranian Society of Nephrology, in collaboration with the International Society of Nephrology (ISN). RESULTS: Overall, neural networks have more robust results and given that they can be trained on more data to gain better accuracy, and gain more generalization, they show promising results. Overall, the best specificity that was achieved on testing almost all of the records was 99.24% and the best sensitivity that was achieved in testing almost all of the records was 94.44%. CONCLUSION: We introduced several machine learning-based methods for predicting rhabdomyolysis-induced AKI on the third day after a catastrophic incident. The introduced models show higher accuracy compared to previous works performed on the Bam earthquake dataset.
Tehrani et al. (Fri,) conducted a observational in Rhabdomyolysis-induced acute kidney injury (n=1,440). Machine learning predictive models (Neural Networks, Random Forest, SVM) vs. Traditional clinical thresholds was evaluated on Prediction of acute kidney injury occurrence on the third day. Machine learning models, particularly neural networks, accurately predicted rhabdomyolysis-induced acute kidney injury on the third day after an earthquake, achieving up to 94.44% sensitivity and 99.24% specificity.