论文标题
将生理时间序列和临床笔记与变压器相结合，以早期预测败血症
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis
论文作者
论文摘要
败血症是重症监护病房（ICU）中死亡的主要原因。败血症的早期检测对于患者生存至关重要。在本文中，我们使用生理时间序列数据和临床注释为每位患者提供了ICU入院的$ 36 $小时内的生理时间序列数据和临床笔记，为早期败血症预测提出了多模式变压器模型。具体而言，我们旨在仅使用前12、18、24、30和36小时的实验室测量，生命体征，患者人口统计和临床注释来预测败血症。我们在两个大型重症监护数据集上评估了我们的模型：模仿III和EICU-CRD。将提出的方法与六个基准进行了比较。此外，进行消融分析和案例研究以研究模型的每个组成部分的影响以及每种数据模式对早期败血症预测的贡献。实验结果证明了我们的方法的有效性，该方法的表现优于所有指标的竞争基准。
Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early detection of sepsis is critical for patient survival. In this paper, we propose a multimodal Transformer model for early sepsis prediction, using the physiological time series data and clinical notes for each patient within $36$ hours of ICU admission. Specifically, we aim to predict sepsis using only the first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs, patient demographics, and clinical notes. We evaluate our model on two large critical care datasets: MIMIC-III and eICU-CRD. The proposed method is compared with six baselines. In addition, ablation analysis and case studies are conducted to study the influence of each individual component of the model and the contribution of each data modality for early sepsis prediction. Experimental results demonstrate the effectiveness of our method, which outperforms competitive baselines on all metrics.
