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

这项研究通过使用病人处方数据作为模态性能提高急性肾损伤预测的现有模型的新方法，采用电子健康记录（EHR）数据提取相关病人处方信息，将其转化为所选药物表示法（扩展连接指纹）进行研究，并采用独特的多模态方法，应用于临床药物表示，建立了一个在预测急性肾损伤方面从未被任何研究所使用的程序。结果表明，通过将扩展连接指纹等药物特征与人口统计学和实验室检测值等常见队列特征结合起来，我们在急性肾损伤预测任务的模型性能中取得了明显的改善，超过了不包括药物表示作为特征的基线模型，这表明我们的独特方法增强了现有的基准技术，凸显了在重症监护设置中预测急性肾损伤中药物数据的相关性。

在重症监护室通过整合药物特征提升急性肾损伤预测