  Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the
rapid loss of kidney excretory function, which aggravates the clinical severity
of other diseases in a large number of hospitalized patients. Accurate early
prediction of AKI can enable in-time interventions and treatments. However, AKI
is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to
an improved understanding of the disease pathophysiology and development of
more targeted clinical interventions. This study used a memory network-based
deep learning approach to discover AKI sub-phenotypes using structured and
unstructured electronic health record (EHR) data of patients before AKI
diagnosis. We leveraged a real world critical care EHR corpus including 37,486
ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype
I is with an average age of 63.03$ \pm 17.25 $ years, and is characterized by
mild 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 was
characterized 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 likely
to develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm
11.32 $ years, and was characterized moderate loss of kidney excretory function
and 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 significantly
different across the three sub-phenotypes with statistical testing plus postdoc
analysis, and the conclusion still holds after age adjustment.

Machine Learning;Machine Learning