Acute kidney injury (AKI) is defined by a rapid deterioration in kidney function based on the rate of change in a patient’s estimated glomerular filtration values. This is a code for alternative automated approaches for detecting AKI, i.e., novel Surrey AKI detection algorithm (SAKIDA)
We introduced a novel algorithm “SAKIDA” to detect AKIs from the primary care data. The proposed SAKIDA performs better than GPR and NHS England algorithms in the primary care settings with 70% accuracy. GPR and NHS England are more suitable in real-time systems e.g., in secondary care settings.
@inproceedings{tirunagari2016automatic, title={Automatic detection of acute kidney injury episodes from primary care data}, author={Tirunagari, Santosh and Bull, Simon C and Vehtari, Aki and Farmer, Christopher and de Lusignan, Simon and Poh, Norman}, booktitle={Computational Intelligence (SSCI), 2016 IEEE Symposium Series on}, pages={1--6}, year={2016}, organization={IEEE} }