  The relationship between acute kidney injury (AKI) prediction and nephrotoxic
drugs, or drugs that adversely affect kidney function, is one that has yet to
be explored in the critical care setting. One contributing factor to this gap
in research is the limited investigation of drug modalities in the intensive
care unit (ICU) context, due to the challenges of processing prescription data
into the corresponding drug representations and a lack in the comprehensive
understanding of these drug representations. This study addresses this gap by
proposing a novel approach that leverages patient prescription data as a
modality to improve existing models for AKI prediction. We base our research on
Electronic Health Record (EHR) data, extracting the relevant patient
prescription information and converting it into the selected drug
representation for our research, the extended-connectivity fingerprint (ECFP).
Furthermore, we adopt a unique multimodal approach, developing machine learning
models and 1D Convolutional Neural Networks (CNN) applied to clinical drug
representations, establishing a procedure which has not been used by any
previous studies predicting AKI. The findings showcase a notable improvement in
AKI prediction through the integration of drug embeddings and other patient
cohort features. By using drug features represented as ECFP molecular
fingerprints along with common cohort features such as demographics and lab
test values, we achieved a considerable improvement in model performance for
the AKI prediction task over the baseline model which does not include the drug
representations as features, indicating that our distinct approach enhances
existing baseline techniques and highlights the relevance of drug data in
predicting AKI in the ICU setting
