All authors: Department of Electronics and Communication Engineering, National Institute of Technology Goa, Ponda, India.
Crit Care Med. 2020 Dec;48(12):e1343-e1349. doi: 10.1097/CCM.0000000000004691.
Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and machine learning techniques for early prediction of sepsis.
An algorithm for early identification of sepsis using ratio and power-based feature transformation of easily obtainable clinical data.
PhysioNet Challenge 2019 provided ICU data from three separate hospital systems. Publicly shared data from two hospital systems are used for training and validation purposes, whereas sequestered data from all the three systems is used for testing.
Over 60,000 ICU patients with up to 40 clinical variables are sourced for each hour of their ICU stay. The Sepsis-3 criterion is applied for annotation.
None.
The clinical feature exploration for early prediction of sepsis is achieved using the proposed framework named genetic algorithm optimized ratio and power-based expert algorithm. An optimal feature set containing 46 ratio and power-based features is computed from the given patient covariates using genetic algorithm optimized ratio and power-based expert and grouped with identified 17 raw features and 55 statistical features to form a final feature set of 118 clinical features to predict the onset of sepsis in the proceeding 6 hours. The obtained features are fed to a hybrid Random Under-Sampling-Boosting algorithm, called RUSBoost for alleviating the involved class imbalance. The optimal RUSBoost model has achieved a normalized utility score of 0.318 on full test data.
The proposed study supports the realization of a hospital-specific customized solution in the form of an early-warning system for sepsis. However, an extended analysis is necessary to apply this framework for hospital-independent diagnosis of sepsis in general. Nevertheless, the clinical utility of hospital-specific customized solutions based on the proposed method across a wide range of hospital systems needs to be studied.
早期预测脓毒症对于提供早期最佳治疗至关重要。本研究旨在采用软计算和机器学习技术进行脓毒症早期预测。
一种基于易获得临床数据的比和幂特征变换的算法，用于早期识别脓毒症。
PhysioNet 挑战赛 2019 提供了来自三个不同医院系统的 ICU 数据。使用来自两个医院系统的公开共享数据进行训练和验证，而使用所有三个系统的隔离数据进行测试。
每个 ICU 患者的 ICU 入住时间长达 40 个临床变量，每个小时可获得超过 60,000 个 ICU 患者的数据。应用 Sepsis-3 标准进行注释。
无。
使用名为遗传算法优化比和幂专家算法的提出框架进行脓毒症早期预测的临床特征探索。从给定的患者协变量中使用遗传算法优化比和幂专家计算包含 46 个比和幂特征的最优特征集，并与确定的 17 个原始特征和 55 个统计特征组合，形成最终包含 118 个临床特征的特征集，以预测随后 6 小时内脓毒症的发生。将获得的特征输入到一种混合随机欠采样-提升算法（称为 RUSBoost）中，以缓解所涉及的类别不平衡问题。最优 RUSBoost 模型在全测试数据上的归一化效用评分达到 0.318。
该研究支持以脓毒症预警系统的形式实现医院特定的定制解决方案。然而，需要进行扩展分析，以将该框架应用于一般的医院独立脓毒症诊断。然而，需要研究基于所提出的方法的医院特定定制解决方案在广泛的医院系统中的临床应用。
