We have developed a novel AKI diagnostic algorithm upon KID 2009 database. The KID is multi-featured and the AKI and non-AKI groups are highly imbalanced, making it challenging to describe them via simple linear statistics. Thus, to identify features effectively, our AKI association studies employed statistical learning strategies; a predictive model was created to accurately determine which KID data elements were highly associated with an AKI diagnosis. We employed prediction analysis of microarrays (PAM), which is commonly applied to high-feature datasets such as DNA microarrays; PAM determines which data elements, or features, best contribute to the predictive model or characterize individual classes/cohorts, Clinical Classification Software codes (286 diagnosis, 231 procedural) were used to bin ICD-9-CM codes (n=6,722) and analyzed by PAM. PAM identified relevant AKI predictors and eliminated irrelevant data elements, which constitute noise.
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