Acute brain dysfunctions (ABD), which include coma and delirium, are prevalent in the ICU, especially among older patients. The current approach in manual assessment of ABD by care providers may be sporadic and subjective. Hence, there exists a need for a data-driven robust system automating the assessment and prediction of ABD. In this work, we develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data. Our data processing pipeline enables integration of static and temporal data, and extraction of features relevant to ABD. We train several state-of-the-art transformer models and baseline machine learning models including CatBoost and XGB on the data that was collected from patients admitted to the ICU at UF Shands Hospital. We demonstrate the efficacy of our system for tasks related to acute brain dysfunction including binary classification of brain acuity and multi-class classification (i.e., coma, delirium, death, or normal), achieving a mean AUROC of 0.953 on our Long-former implementation. Our system can then be deployed for real-time prediction of ADB in ICUs to reduce the number of incidents caused by ABD. Moreover, the real-time system has the potential to reduce costs, duration of patients stays in the ICU, and mortality among those afflicted.

翻译：急性脑功能障碍（ABD），包括昏迷和谵妄，在重症监护室（ICU）中普遍存在，尤其在老年患者中更为常见。当前护理人员对ABD的人工评估方法可能存在偶然性和主观性。因此，需要一种数据驱动的稳健系统来自动化评估和预测ABD。在本研究中，我们开发了一个利用电子健康记录（EHR）数据进行急性脑功能障碍实时预测的机器学习系统。我们的数据处理流程能够整合静态与时间序列数据，并提取与ABD相关的特征。我们基于佛罗里达大学山兹医院ICU收治患者的数据，训练了多个先进 Transformer 模型及基线机器学习模型（包括CatBoost和XGBoost）。在急性脑功能障碍相关任务中，我们验证了系统的有效性，包括脑功能状态的二分类及多分类（即昏迷、谵妄、死亡或正常），其中基于Long-former的实现取得了0.953的平均AUROC。该系统可部署于ICU进行ABD的实时预测，以减少由ABD引发的不良事件。此外，该实时系统有望降低相关患者的医疗成本、缩短ICU住院时间并减少死亡率。