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Acute Kidney Injury (AKI) is a common clinical syndrome characterized by therapid loss of kidney excretory function, which aggravates the clinical severityof other diseases in a large number of hospitalized patients. Accurate earlyprediction of AKI can enable in-time interventions and treatments. However, AKIis highly heterogeneous, thus identification of AKI sub-phenotypes can lead toan improved understanding of the disease pathophysiology and development ofmore targeted clinical interventions. This study used a memory network-baseddeep learning approach to discover AKI sub-phenotypes using structured andunstructured electronic health record (EHR) data of patients before AKIdiagnosis. We leveraged a real world critical care EHR corpus including 37,486ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotypeI is with an average age of 63.03$ \pm 17.25 $ years, and is characterized bymild loss of kidney excretory function (Serum Creatinine (SCr) $1.55\pm 0.34$mg/dL, estimated Glomerular Filtration Rate Test (eGFR) $107.65\pm 54.98$mL/min/1.73$m^2$). These patients are more likely to develop stage I AKI.Sub-phenotype II is with average age 66.81$ \pm 10.43 $ years, and wascharacterized by severe loss of kidney excretory function (SCr $1.96\pm 0.49$mg/dL, eGFR $82.19\pm 55.92$ mL/min/1.73$m^2$). These patients are more likelyto develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm11.32 $ years, and was characterized moderate loss of kidney excretory functionand thus more likely to develop stage II AKI (SCr $1.69\pm 0.32$ mg/dL, eGFR$93.97\pm 56.53$ mL/min/1.73$m^2$). Both SCr and eGFR are significantlydifferent across the three sub-phenotypes with statistical testing plus postdocanalysis, and the conclusion still holds after age adjustment.

Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks