Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learning architecture were used to construct prediction models. The configurations that utilized both structured and unstructured clinical features yielded competitive F-measure of 0.512. Our results showed that the approaches integrating both structured and unstructured clinical features can be effectively applied to assist clinicians in identifying the risk of mortality in sepsis patients upon admission to the ICU.

翻译：塞普斯是导致死亡的一个重要原因,特别是在特护单位(ICU)病人中。开发查明早期死亡率的新方法对于改善败血病人的生存结果至关重要。我们利用MIMIC-III数据库,综合了人口数据、生理测量和临床说明。我们建立并应用了数个机器学习模型来预测医院死亡率和败血病人30天死亡率的风险。从临床说明中,我们产生了具有临床意义的字面表达和嵌入。我们利用了监督学习分类和深层学习结构来构建预测模型。使用结构化和非结构化临床特征的配置产生了0.512的竞争性F计量。我们的结果表明,将结构化和非结构化临床特征结合起来的方法可以有效地用于协助临床医生识别进入综合体时患败血病人的死亡率风险。