论文标题
血液图数据作为COVID-19管理中决策的工具：资源稀缺方案的应用
Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios
论文作者
论文摘要
Covid-19-Pandemics在全球范围内提出了紧急响应系统的挑战，并广泛报道了基本的服务细分和医疗保健结构的崩溃。关键因素涉及基本的劳动力管理，因为当前的协议建议在包括基本人员在内的有症状人士的职责中释放职责。在几个国家 /地区，诊断要求多数的本地测试能力在几个国家 /地区也有问题。这项工作描述了一种从有症状的患者进行的血液图检查中得出的机器学习模型，以及如何用于预测QRT-PCR测试结果。方法：提出了用于处理不同稀缺情景的机器学习模型，包括管理症状基本的劳动力和缺乏诊断测试。在未执行后者或尚未可用结果的情况下，使用血液图结果数据预测QRT-PCR结果。根据实际预测上下文，对假定的先前概率进行调整允许对模型进行微调。提出的模型可以预测COVID-19 QRT-PCR会导致具有高精度，灵敏度和特异性的有症状患者。数据评估可以根据所需的结果以个人或同时进行。基于血液图数据和背景稀缺环境，与随机选择相比，观察到基于模型的患者选择时，资源分布得到了显着优化。该模型可以帮助管理测试缺乏症和其他关键情况。机器学习模型可以从广泛可用，快速和廉价的考试数据中得出，以预测COVID-19诊断中使用的QRT-PCR结果。这些模型可用于在资源稀缺情况下有助于战略决策，包括人员短缺，缺乏医疗资源和测试不足。
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naive-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.
