School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
Department of Computer Science and Engineering, V R Siddhartha Engineering College, Vijayawada, India.
Big Data. 2023 Dec;11(6):408-419. doi: 10.1089/big.2022.0028. Epub 2022 Sep 13.
Deep learning models deliver a fast diagnosis during triage prescreening for COVID-19 patients, reducing waiting time for hospital admission during health emergency scenarios. The Ministry of health and family welfare government of India provides guidelines from the Indian Council of Medical Research (ICMR) for triage requirements and emergency response with faster allotment of oxygen beds for COVID-19 patients requiring immediate treatment in Tamil Nadu, India. A combination of pretrained models provides a faster screening rate and finds patients with severe lung infections who need to be attended to and allotted oxygen beds. Deep learning (DL) algorithms need to be accurate in triaging undifferentiated patients entering the emergency care system (ECS). The major goal of this work is to analyze the accuracy of machine learning approaches in their application to triage the acuity of patients arriving in the ECS. The proposed triage model has an accuracy of 93% in classifying COVID/non-COVID patients. The proposed triage DL model effectively reduces the time for the triage procedure and streamlines screening and allocation of beds for patients with high risk.
深度学习模型在 COVID-19 患者分诊预筛查期间提供快速诊断，减少了卫生紧急情况下住院的等待时间。印度卫生和家庭福利部政府根据印度医学研究理事会 (ICMR) 的指南，为分诊要求和应急响应提供了指导，以便在印度泰米尔纳德邦更快地为需要立即治疗的 COVID-19 患者分配氧气床。预训练模型的组合提供了更快的筛查率，并发现需要治疗和分配氧气床的严重肺部感染的患者。深度学习 (DL) 算法需要在分诊进入急诊护理系统 (ECS) 的未分化患者时具有准确性。这项工作的主要目标是分析机器学习方法在应用于分诊进入 ECS 的患者的紧迫性方面的准确性。所提出的分诊模型在对 COVID/非 COVID 患者进行分类方面的准确率为 93%。所提出的分诊深度学习模型有效地减少了分诊程序的时间，并简化了对高风险患者的筛查和床位分配。
