Rafiei Alireza, Rezaee Alireza, Hajati Farshid, Gheisari Soheila, Golzan Mojtaba
Intelligent Mobile Robot Lab (IMRL), Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.
College of Engineering and Science, Victoria University Sydney, Australia.
Comput Biol Med. 2021 Jan;128:104110. doi: 10.1016/j.compbiomed.2020.104110. Epub 2020 Nov 10.
Sepsis is a life-threatening condition that occurs due to the body's reaction to infections, and it is a leading cause of morbidity and mortality in hospitals. Early prediction of sepsis onset facilitates early interventions that promote the survival of suspected patients. However, reliable and intelligent systems for predicting sepsis are scarce.
This paper presents a novel technique called Smart Sepsis Predictor (SSP) to predict sepsis onset in patients admitted to an intensive care unit (ICU). SSP is a deep neural network architecture that encompasses long short-term memory (LSTM), convolutional, and fully connected layers to achieve early prediction of sepsis. SSP can work in two modes; Mode 1 uses demographic data and vital signs, and Mode 2 uses laboratory test results in addition to demographic data and vital signs. To evaluate SSP, we have used the 2019 PhysioNet/CinC Challenge dataset, which includes the records of 40,366 patients admitted to the ICU.
To compare SSP with existing state-of-the-art methods, we have measured the accuracy of the SSP in 4-, 8-, and 12-h prediction windows using publicly available data. Our results show that the SSP performance in Mode 1 and Mode 2 is much higher than existing methods, achieving an area under the receiver operating characteristic curve (AUROC) of 0.89 and 0.92, 0.88 and 0.87, and 0.86 and 0.84 for 4 h, 8 h, and 12 h before sepsis onset, respectively.
Using ICU data, sepsis onset can be predicted up to 12 h in advance. Our findings offer an early solution for mitigating the risk of sepsis onset.
脓毒症是一种因机体对感染的反应而出现的危及生命的病症，是医院发病和死亡的主要原因。早期预测脓毒症的发作有助于进行早期干预，从而提高疑似患者的生存率。然而，用于预测脓毒症的可靠且智能的系统却很稀缺。
本文提出了一种名为智能脓毒症预测器（SSP）的新技术，用于预测入住重症监护病房（ICU）的患者的脓毒症发作。SSP是一种深度神经网络架构，包含长短期记忆（LSTM）、卷积和全连接层，以实现对脓毒症的早期预测。SSP可以在两种模式下工作；模式1使用人口统计学数据和生命体征，模式2除人口统计学数据和生命体征外还使用实验室检查结果。为了评估SSP，我们使用了2019年生理网/计算智能挑战赛数据集，该数据集包含40366名入住ICU的患者的记录。
为了将SSP与现有的最先进方法进行比较，我们使用公开可用数据在4小时、8小时和12小时预测窗口中测量了SSP的准确性。我们的结果表明，模式1和模式2下的SSP性能远高于现有方法，在脓毒症发作前4小时、8小时和12小时的受试者工作特征曲线下面积（AUROC）分别达到0.89和0.92、0.88和0.87、0.86和0.84。
利用ICU数据，可以提前12小时预测脓毒症的发作。我们的研究结果为降低脓毒症发作风险提供了早期解决方案。
