Early Detection of Rhabdomyolysis-Induced Acute Kidney Injury through Machine Learning Approaches
Archives of Academic Emergency Medicine,
Vol. 9 No. 1 (2021),
1 January 2021
,
Page e29
https://doi.org/10.22037/aaem.v9i1.1059
Abstract
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.
- Acute Kidney Injury
- Clinical Decision Rules
- Machine Learning
- Neural Networks
- Decision Making
- Computer
How to Cite
References
Ito J, Fukagawa M. Predicting the risk of acute kidney injury in earthquake victims. Nature Clinical Practice Nephrology. 2009;5(2):64-5.
Aronson S, Fontes ML, Miao Y, Mangano DT. Risk Index for Perioperative Renal Dysfunction/Failure. Circulation. 2007;115(6):733-42.
Mehran R, Aymong ED, Nikolsky E, Lasic Z, Iakovou I, Fahy M, et al. A simple risk score for prediction of contrast-induced nephropathy after percutaneous coronary intervention: development and initial validation. Journal of the American College of Cardiology. 2004;44(7):1393-9.
Wijeysundera DN, Karkouti K, Dupuis J-Y, Rao V, Chan CT, Granton JT, et al. Derivation and validation of a simplified predictive index for renal replacement therapy after cardiac surgery. Jama. 2007;297(16):1801-9.
Palomba H, De Castro I, Neto A, Lage S, Yu L. Acute kidney injury prediction following elective cardiac surgery: AKICS Score. Kidney international. 2007;72(5):624-31.
Najafi I, Van Biesen W, Sharifi A, Hoseini M, Rashid Farokhi F, Sanadgol H, et al. Early detection of patients at high risk for acute kidney injury during disasters: development of a scoring system based on the Bam earthquake experience. J Nephrology. 2008;21(5):776-82.
Keras: the Python deep learning API [Available from: https://keras.io/.
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv: 160304467. 2016.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research. 2014;15(1):1929-58.
Wang J, Chen Q, Chen Y, editors. RBF kernel based support vector machine with universal approximation and its application. International symposium on neural networks; 2004: Springer.
Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv e-prints. 2015.
Lazic S. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn.
- Abstract Viewed: 1086 times
- pdf Downloaded: 509 times
