DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration
DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration
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DOI：
10.1016/j.eswa.2020.113905
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发表时间：
2021-03-01
影响因子：
8.5
通讯作者：
Eskofier, Bjoern
中科院分区：
文献类型：
--
作者：
da Silva, Denise Bandeira;Schmidt, Diogo;Eskofier, Bjoern
危重患者的早期诊断取决于医务人员对不同变量的关注和观察，如生命体征，实验室检查结果等。重症患者在病情恶化之前，通常会出现生命体征的变化。监测这些变化对于预测诊断以启动患者护理非常重要。预后指标在这方面发挥着重要作用，因为它们可以估计患者的健康状况。此外，电子健康记录的采用提高了数据的可用性，这些数据可以通过机器学习技术进行处理，以提取信息，支持临床决策。在这种情况下，这项工作的目的是创建一个计算模型，能够预测患者健康状况的恶化，以便尽快开始适当的治疗。该模型是基于深度学习技术、循环神经网络、长短期记忆开发的，用于预测患者的生命体征，并通过健康领域常用的预后指数对患者的健康状况严重程度进行后续评估。实验表明，可以以良好的精度（准确度> 80%）预测生命体征，并且因此可以提前预测预后指数以在恶化之前治疗患者。预测患者未来的生命体征并将其用于预后指数的计算允许临床时间预测应用当前患者的生命体征将不可能的未来严重诊断（50%-60%的病例将不能被识别）。
Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients' care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients' health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients' health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep Learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient's vital signs and subsequent evaluation of the patient's health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy > 80%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient's vital signs for the future and use them for the Prognostic Index' calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient's vital signs (50%-60% of cases would not be identified).