论文标题
基于患者调查数据的COVID-19诊断的分类
Classification supporting COVID-19 diagnostics based on patient survey data
论文作者
论文摘要
由于模棱两可的症状和医生的最初经历，将Covid-19与其他类似流感的疾病区分开可能很困难。鉴于，至关重要的是要过滤那些不需要对SARS-COV-2感染进行测试的病患者，尤其是在疾病压倒性的情况下。作为介绍的研究的一部分，产生了有效筛查COVID-19的逻辑回归和XGBoost分类器。调整了每种方法以达到分类过程中假定的负预测值的可接受阈值。此外，提出了对获得的分类模型的解释。该解释使用户能够了解模型决定的基础。获得的分类模型为解码服务（Decode.polsl.pl）提供了基础，可以作为筛查Covid-19患者的支持。此外，研究社区提供了构成进行分析的基础的数据集。该数据集由3,000多个示例组成，基于波兰医院收集的问卷。
Distinguishing COVID-19 from other flu-like illnesses can be difficult due to ambiguous symptoms and still an initial experience of doctors. Whereas, it is crucial to filter out those sick patients who do not need to be tested for SARS-CoV-2 infection, especially in the event of the overwhelming increase in disease. As a part of the presented research, logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19, were generated. Each of the methods was tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models was presented. The explanation enables the users to understand what was the basis of the decision made by the model. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set consisting of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
