血象数据作为COVID-19管理中的决策工具：在资源稀缺情况下的应用
Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios
摘要：
2019冠状病毒病大流行挑战了全球的应急响应系统，广泛报道基本服务中断和医疗保健结构崩溃。一个关键因素涉及基本的劳动力管理，因为目前的协议建议解除症状个人的职务，包括必要的人员。一些国家的检测能力也存在问题，诊断需求超过了当地现有的检测能力。这项工作描述了一个机器学习模型，该模型来源于在有症状的患者中进行的血象检查数据，以及它们如何用于预测qRT-PCR检测结果。研究方法：提出了一种用于机器学习的朴素贝叶斯模型，用于处理不同的稀缺场景，包括管理有症状的必要劳动力和缺乏诊断测试。血象结果数据用于预测qRT-PCR结果，如果未进行qRT-PCR或结果尚不可用。假设先验概率的调整允许根据实际预测上下文对模型进行微调。提出的模型可以预测有症状个体的COVID-19 qRT-PCR结果，具有较高的准确性，灵敏度和特异性。数据评估可根据预期结果单独或同时进行。基于血象数据和背景稀缺背景，与随机选择相比，当观察到基于模型的患者选择时，资源分配被显著优化。该模型可以帮助管理测试缺陷和其他关键情况。机器学习模型可以从广泛可用、快速且廉价的检查数据中获得，以预测用于COVID-19诊断的qRT-PCR结果。这些模型可用于在资源稀缺的情况下辅助战略决策，包括人员短缺，医疗资源缺乏和测试不足。
英文摘要：
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.
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