  Drug representations have played an important role in cheminformatics.
However, in the healthcare domain, drug representations have been underused
relative to the rest of Electronic Health Record (EHR) data, due to the
complexity of high dimensional drug representations and the lack of proper
pipeline that will allow to convert clinical drugs to their representations.
Time-varying vital signs, laboratory measurements, and related time-series
signals are commonly used to predict clinical outcomes. In this work, we
demonstrated that using clinical drug representations in addition to other
clinical features has significant potential to increase the performance of
mortality and length of stay (LOS) models. We evaluate the two different drug
representation methods (Extended-Connectivity Fingerprint-ECFP and
SMILES-Transformer embedding) on clinical outcome predictions. The results have
shown that the proposed multimodal approach achieves substantial enhancement on
clinical tasks over baseline models. Using clinical drug representations as
additional features improve the LOS prediction for Area Under the Receiver
Operating Characteristics (AUROC) around %6 and for Area Under Precision-Recall
Curve (AUPRC) by around %5. Furthermore, for the mortality prediction task,
there is an improvement of around %2 over the time series baseline in terms of
AUROC and %3.5 in terms of AUPRC. The code for the proposed method is available
at https://github.com/tanlab/MIMIC-III-Clinical-Drug-Representations.
