A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients.
A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients.
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基于时间序列特征的递归分类模型优化治疗策略以改善COVID-19患者的预后和资源分配
DOI：
10.1109/jbhi.2021.3139773
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发表时间：
2022-07
影响因子：
7.7
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中科院分区：
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本文提出了一种基于特征时间序列数据的新型Lasso Logistic回归模型，用于确定2019冠状病毒病（COVID-19）患者的疾病严重程度以及何时给予药物或升级干预程序。从住院COVID-19患者的高度富集和时间序列生命体征数据（包括血氧饱和度读数）中提取高级特征，并结合患者人口统计学和合并症信息，作为基于动态特征的分类模型的输入。这种动态组合带来了深刻的见解，以指导复杂COVID-19病例的临床决策，包括预后预测，药物给药时间，入住重症监护室以及通气和插管等干预程序的应用。COVID-19患者分类模型是利用美国德克萨斯州一家领先的多医院系统中的900名住院COVID-19患者开发的。通过基于个体COVID-19患者的时间序列生理数据、人口统计学和临床记录提供死亡率预测，基于动态特征的分类模型可用于提高COVID-19患者治疗的功效、优先考虑医疗资源并减少伤亡。我们模型的独特之处在于它仅基于前24小时的生命体征数据，因此可以及早决定并有效应用临床干预措施。这一战略可以扩展到为未来的大流行病事件优先分配资源和药物治疗。
This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for future pandemic events.