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Electronic Health Records (EHRs) have been heavily used to predict variousdownstream clinical tasks such as readmission or mortality. One of themodalities in EHRs, clinical notes, has not been fully explored for these tasksdue to its unstructured and inexplicable nature. Although recent advances indeep learning (DL) enables models to extract interpretable features fromunstructured data, they often require a large amount of training data. However,many tasks in medical domains inherently consist of small sample data withlengthy documents; for a kidney transplant as an example, data from only a fewthousand of patients are available and each patient's document consists of acouple of millions of words in major hospitals. Thus, complex DL methods cannotbe applied to these kinds of domains. In this paper, we present a comprehensiveensemble model using vector space modeling and topic modeling. Our proposedmodel is evaluated on the readmission task of kidney transplant patients andimproves 0.0211 in terms of c-statistics from the previous state-of-the-artapproach using structured data, while typical DL methods fail to beat thisapproach. The proposed architecture provides the interpretable score for eachfeature from both modalities, structured and unstructured data, which is shownto be meaningful through a physician's evaluation.

Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission