University of Chinese Academy of Sciences, Beijing, China.
State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Crit Care Med. 2020 Dec;48(12):e1337-e1342. doi: 10.1097/CCM.0000000000004644.
Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records.
Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation.
The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network.
Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item.
The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and -0.215 on test set A, B, and C, respectively).
We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.
脓毒症是由感染和随后的免疫系统过度反应引起的，严重威胁着人类的生命。早期预测对脓毒症的治疗很重要。本报告旨在基于临床电子健康记录，开发一种提前 6 小时预测脓毒症的方法。
挑战赛数据由 PhysioNet/Computing in Cardiology Challenge 2019 发布，来自三个不同医院系统的 ICU 患者。两个数据集的一部分数据，包括 40336 名受试者，是公开的，其余的则作为隐藏测试集使用。由组委会定义的归一化效用评分用于模型性能评估。
应用监督机器学习来解决这一挑战。具体来说，我们在集成学习框架下建立预测模型，通过集成基于脓毒症临床先验知识的人工特征和长短期记忆神经网络自动提取的深度特征来实现。
对每个个体每小时测量和记录 40 个临床变量，包括 8 个生命体征、26 个实验室值和 6 个人口统计学指标，并同时为每个项目提供二进制标签（0 或 1）。
通过 30 折交叉验证评估所提出的模型。在公开数据集上，敏感性、特异性和归一化效用评分分别为 0.641±0.022、0.844±0.007 和 0.401±0.019。我们团队（UCAS_DataMiner）在全隐藏测试集上获得的最终归一化效用评分是 0.313（分别在测试集 A、B 和 C 上为 0.406、0.373 和-0.215）。
我们通过集成学习实现了基于临床电子健康记录的提前 6 小时预测脓毒症。结果表明，所提出的模型在脓毒症的早期预测中表现良好。特别是，集成学习在性能上比任何单一模型都有显著的（p<0.01）提高。
