摘要翻译：
新冠肺炎疫情对全球应急系统提出了挑战，广泛报道了基本服务崩溃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有症状的个人的职务，包括基本人员。在一些国家，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从有症状患者的血象检查数据中导出的机器学习模型，以及如何使用它们来预测qRT-PCR检测结果。方法：提出了一个用于机器学习的朴素贝叶斯模型，用于处理不同的稀缺性场景，包括管理有症状的基本劳动力和缺乏诊断测试。血象结果数据用于预测qRT-PCR结果的情况下，后者没有进行，或结果尚未得到。在假定的先验概率中的调整允许根据实际预测上下文对模型进行微调。所提出的模型可以预测有症状个体的新冠肺炎qRT-PCR结果，具有较高的准确性、敏感性和特异性。根据期望的结果，可以在单独或同时的基础上进行数据评估。基于血象数据和背景稀缺性背景，与随机选择相比，基于模型的患者选择可以显著优化资源分配。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、快速和廉价的检查数据中导出，以预测新冠肺炎诊断中使用的qRT-PCR结果。这些模型可用于辅助资源稀缺场景下的战略决策，包括人员短缺、医疗资源缺乏和检测不足。
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英文标题：
《Hemogram Data as a Tool for Decision-making in COVID-19 Management:
  Applications to Resource Scarcity Scenarios》
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作者：
Eduardo Avila, Marcio Dorn, Clarice Sampaio Alho, Alessandro Kahmann
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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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PDF链接：
https://arxiv.org/pdf/2005.10227