Li Yikuan, Yao Liang, Mao Chengsheng, Srivastava Anand, Jiang Xiaoqian, Luo Yuan
Dept. of EECS, Northwestern University, Evanston, IL, U.S.A.
Dept. of Preventive Medicine, Northwestern University, Chicago, IL, U.S.A.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:683-686. doi: 10.1109/bibm.2018.8621574. Epub 2019 Jan 24.
Acute kidney injury (AKI) in critically ill patients is associated with significant morbidity and mortality. Development of novel methods to identify patients with AKI earlier will allow for testing of novel strategies to prevent or reduce the complications of AKI. We developed data-driven prediction models to estimate the risk of new AKI onset. We generated models from clinical notes within the first 24 hours following intensive care unit (ICU) admission extracted from Medical Information Mart for Intensive Care III (MIMIC-III). From the clinical notes, we generated clinically meaningful word and concept representations and embeddings, respectively. Five supervised learning classifiers and knowledge-guided deep learning architecture were used to construct prediction models. The best configuration yielded a competitive AUC of 0.779. Our work suggests that natural language processing of clinical notes can be applied to assist clinicians in identifying the risk of incident AKI onset in critically ill patients upon admission to the ICU.
危重症患者的急性肾损伤（AKI）与显著的发病率和死亡率相关。开发能够更早识别AKI患者的新方法，将有助于测试预防或减少AKI并发症的新策略。我们开发了数据驱动的预测模型来估计新发AKI的风险。我们从重症监护医学信息集市III（MIMIC-III）中提取了重症监护病房（ICU）入院后最初24小时内的临床记录来生成模型。从临床记录中，我们分别生成了具有临床意义的单词和概念表示及嵌入。使用五个监督学习分类器和知识引导的深度学习架构来构建预测模型。最佳配置产生了具有竞争力的0.779的曲线下面积（AUC）。我们的工作表明，临床记录的自然语言处理可用于协助临床医生在ICU入院时识别危重症患者发生AKI的风险。
