论文标题
脱颖而出的症状集合的信息排名：一种新的数据驱动方法，以识别Covid的强烈警告信号19
Informative Ranking of Stand Out Collections of Symptoms: A New Data-Driven Approach to Identify the Strong Warning Signs of COVID 19
论文作者
论文摘要
我们在这里开发了一种基于给定症状的数据驱动疾病识别的方法，以成为异常检测的有效工具。在临床环境中，当与特征结合的患者展示时，医生可能会想知道某种症状的组合是否特别预测，例如“女性对男性的狂热程度更大？”这个问题的答案是，是的。我们在这里开发了一种列举此类问题的方法，以了解试图诊断疾病（称为条件预测信息的疾病）时的更强警告信号（CPI），我们称之为CPIR。这种易于使用的过程使我们能够确定症状和特征的特别内容组合，这些症状和特征可能有助于医疗领域分析，并可能成为一种新的数据驱动的建议方法，以进行个人医学诊断以及更广泛的公共政策讨论。特别是，由于19号大流行，我们一直在激励在当前的紧迫世界危机中开发此工具。我们在此处将这些方法应用于从国家，省和市政健康报告中收集的数据，以及从在线的其他信息，然后策划为在线公开可用的GitHub存储库。
We develop here a data-driven approach for disease recognition based on given symptoms, to be efficient tool for anomaly detection. In a clinical setting and when presented with a patient with a combination of traits, a doctor may wonder if a certain combination of symptoms may be especially predictive, such as the question, "Are fevers more informative in women than men?" The answer to this question is, yes. We develop here a methodology to enumerate such questions, to learn what are the stronger warning signs when attempting to diagnose a disease, called Conditional Predictive Informativity, (CPI), whose ranking we call CPIR. This simple to use process allows us to identify particularly informative combinations of symptoms and traits that may help medical field analysis in general, and possibly to become a new data-driven advised approach for individual medical diagnosis, as well as for broader public policy discussion. In particular we have been motivated to develop this tool in the current enviroment of the pressing world crisis due to the COVID 19 pandemic. We apply the methods here to data collected from national, provincial, and municipal health reports, as well as additional information from online, and then curated to an online publically available Github repository.
