Department of Industrial Engineering and Management, Afeka Academic College of Engineering, Tel Aviv, Israel.
Department of General Intensive Care and Institute for Nutrition Research, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
J Healthc Eng. 2019 Nov 3;2019:5930379. doi: 10.1155/2019/5930379. eCollection 2019.
Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical outcome is more probable when onset is suspected and treated on time, thus early insight of sepsis onset may save lives and reduce costs.
We present a novel approach for feature extraction, which focuses on the hypothesis that unstable patients are more prone to develop sepsis during ICU stay. These features are used in machine learning algorithms to provide a prediction of a patient's likelihood to develop sepsis during ICU stay, hours before it is diagnosed.
Five machine learning algorithms were implemented using R software packages. The algorithms were trained and tested with a set of 4 features which represent the variability in vital signs. These algorithms aimed to calculate a patient's probability to become septic within the next 4 hours, based on recordings from the last 8 hours. The best area under the curve (AUC) was achieved with Support Vector Machine (SVM) with radial basis function, which was 88.38%.
The high level of predictive accuracy along with the simplicity and availability of input variables present great potential if applied in ICUs. Variability of a patient's vital signs proves to be a good indicator of one's chance to become septic during ICU stay.
基于重症监护病房（ICU）床边监测数据实现对脓毒症检测时刻的准确预测。如果能及时怀疑并治疗发病，那么患者获得良好临床结局的可能性更大，因此，早期洞察脓毒症的发病情况可能会挽救生命并降低成本。
我们提出了一种新的特征提取方法，其重点假设是不稳定的患者在 ICU 住院期间更容易发生脓毒症。这些特征被用于机器学习算法中，以提供患者在 ICU 住院期间发生脓毒症的可能性预测，即在确诊前数小时进行预测。
使用 R 软件包实现了五种机器学习算法。这些算法使用一组代表生命体征变化的四个特征进行了训练和测试。这些算法旨在根据患者最后 8 小时的记录，计算患者在接下来的 4 小时内发生脓毒症的概率。支持向量机（SVM）和径向基函数的曲线下面积（AUC）最高，达到 88.38%。
如果将其应用于 ICU，那么其高预测准确性以及输入变量的简单性和可用性具有很大的潜力。患者生命体征的变化证明是 ICU 住院期间发生脓毒症的一个很好的指标。
