Fig. 2

Development of AKI Prediction Algorithm. The first step in the development of an AKI prediction model is feature selection. This process would evaluate known risk factors identified from the literature and would use machine learning techniques to identify novel risk factors from amongst the EHR dataset. All appropriate features would be considered for inclusion in the actual prediction model which would weight individual variables to create a generalizable model. This model would be validated using a different (or subset of existing) dataset. Once validated, the model could then be integrated directly into the EHR to allow real time AKI alerting. Reproduced with permission from ADQI