  Electronic Health Records (EHRs) have been heavily used to predict various
downstream clinical tasks such as readmission or mortality. One of the
modalities in EHRs, clinical notes, has not been fully explored for these tasks
due to its unstructured and inexplicable nature. Although recent advances in
deep learning (DL) enables models to extract interpretable features from
unstructured data, they often require a large amount of training data. However,
many tasks in medical domains inherently consist of small sample data with
lengthy documents; for a kidney transplant as an example, data from only a few
thousand of patients are available and each patient's document consists of a
couple of millions of words in major hospitals. Thus, complex DL methods cannot
be applied to these kinds of domains. In this paper, we present a comprehensive
ensemble model using vector space modeling and topic modeling. Our proposed
model is evaluated on the readmission task of kidney transplant patients and
improves 0.0211 in terms of c-statistics from the previous state-of-the-art
approach using structured data, while typical DL methods fail to beat this
approach. The proposed architecture provides the interpretable score for each
feature from both modalities, structured and unstructured data, which is shown
to be meaningful through a physician's evaluation.
