Garrido Nicolás J, González-Martínez Félix, Losada Susana, Plaza Adrián, Del Olmo Eneida, Mateo Jorge
Internal Medicine, Virgen de la Luz Hospital, 16002 Cuenca, Spain.
Expert Medical Analysis Group, Institute of Technology, University of Castilla-La Mancha, 16071 Cuenca, Spain.
Biomimetics (Basel). 2024 Jul 18;9(7):440. doi: 10.3390/biomimetics9070440.
Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission.
In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm.
The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients.
These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.
人工智能（AI）系统已在医疗保健的各个领域得到应用。同样，它们在医院急诊服务中也能带来诸多优势。这项工作的目的是证明，通过人工智能的创新性应用，可以开发出一个经过训练的系统，该系统能够比标准化分诊系统更快地检测出在新的大流行中存在潜在感染风险的患者。这种识别将在急诊科进行，从而能够尽早实施组织性预防措施以阻断传播链。
在本研究中，我们以新冠疫情期间为例，建议在大流行期间的急诊科分诊中使用机器学习系统，以检测死亡和感染风险最高的患者，在这一时期，快速决策和全面支持变得越来越关键。纳入所有连续到急诊科就诊的患者，并使用极端梯度提升（XGB）算法自动分析89多个变量。
XGB系统的平衡准确率最高，为91.61%。此外，它比传统分诊系统能更快地得出结果。对死亡率预测影响最大的变量是降钙素原水平、年龄和血氧饱和度，其次是乳酸脱氢酶（LDH）水平、C反应蛋白、胸部X线间质浸润的存在情况以及D-二聚体。我们的系统还确定了这些患者中氧疗的重要性。
这些结果突出表明，XGB是分诊系统中一种有用且新颖的工具，可用于指导未来大流行中的护理路径，这是效仿了著名的新冠疫情的范例。
