Wickramaratne Sajila D, Shaad Mahmud M D
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:70-73. doi: 10.1109/EMBC44109.2020.9175223.
Early prediction of sepsis is essential to give the patient timely treatment since each hour of delayed treatment has been associated with an increase in mortality. Current sepsis detection systems rely on empirical Clinical Decision Rules(CDR)s, which are based on vital signs that can be collected from the bedside. The main disadvantages of CDRs include questions of generalizability and performance variance when applied to the populations different from the groups used for derivation and often take years to develop and validate. This paper proposes a deep learning model using Bi-Directional Gated Recurrent Units(GRU), which uses a wide range of parameters that are associated with vitals, laboratory, and demographics of patients. The proposed model has an area under the receiver operating characteristic (AUROC) of 0.97, outperforming all the existing systems in the current literature. The model can handle the missing data, and irregular sampling intervals frequently present in medical records.Clinical relevance-The proposed model can be used to predict the onset of sepsis 6 hours ahead of time by the use of a machine learning algorithm. This proposed method outperforms the sepsis prediction machine learning models found in the current literature.
脓毒症的早期预测对于及时治疗患者至关重要，因为每延迟一小时治疗都与死亡率增加相关。当前的脓毒症检测系统依赖基于经验的临床决策规则（CDR），这些规则基于可在床边收集的生命体征。CDR的主要缺点包括应用于与用于推导的群体不同的人群时的普遍性问题和性能差异，并且通常需要数年时间来开发和验证。本文提出了一种使用双向门控循环单元（GRU）的深度学习模型，该模型使用与患者的生命体征、实验室检查结果和人口统计学相关的广泛参数。所提出的模型在受试者工作特征曲线下面积（AUROC）为0.97，优于当前文献中的所有现有系统。该模型可以处理医疗记录中经常出现的缺失数据和不规则采样间隔。临床相关性——所提出的模型可通过使用机器学习算法提前6小时预测脓毒症的发作。该方法优于当前文献中发现的脓毒症预测机器学习模型。
