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

翻译：急性肾损伤（AKI）预测与肾毒性药物（即对肾功能产生不良影响的药物）之间的关系在重症监护领域中尚待探索。造成这一研究空白的原因之一是：由于将处方数据转化为相应药物表征存在处理难度，且缺乏对这些药物表征的全面理解，导致药物模态在重症监护室（ICU）环境中的研究有限。本研究提出一种创新方法，利用患者处方数据作为模态来改进现有AKI预测模型，从而弥补这一研究空白。我们以电子健康记录（EHR）数据为基础，提取相关患者处方信息并将其转化为研究所选定的药物表征——扩展连接指纹（ECFP）。此外，我们采用独特的多元模态方法，开发应用于临床药物表征的机器学习模型和一维卷积神经网络（CNN），建立了此前任何AKI预测研究均未使用过的流程。研究结果表明，通过整合药物嵌入与患者队列其他特征，AKI预测性能得到显著提升。采用ECFP分子指纹表征的药物特征，结合人口统计学和实验室检测值等常见队列特征，相比未纳入药物表征特征的基线模型，我们在AKI预测任务中实现了模型性能的显著改善，这表明我们独特的方法增强了现有基线技术，并凸显了药物数据在ICU环境下预测AKI中的相关性。