论文标题

使用IT工具来搜索天气因素与肺血栓栓塞发作之间的相关性

Use of IT tools to search for a correlation between weather factors and onset of pulmonary thromboembolism

论文作者

Bernini, Antonio, Bonaccorsi, Lorella, Fanti, Pietro, Ranaldi, Francesco, Santosuosso, Ugo

论文摘要

肺栓塞(PE)和深静脉血栓形成(DVT)以静脉血栓栓塞(VTE)聚集,代表了心血管疾病的第三个原因。最近的研究表明,作为大气压力,温度和湿度的气象参数可能会影响PE发生率,但如今,争论了这两种现象之间的关系,并且没有完全解释证据。 AOUC医院急诊医学系的临床经验表明,关系有效地存在。我们收集了有关PE患者的急诊医学单位入院的数据,以确认我们的假设。同时,从托斯卡纳地区的拉玛联盟收集了大气参数。我们通过使用半小时的天气时间记录高分辨率数据来处理数据集,从而实现了新的IT模型和统计工具。我们已经从计量经济学(例如移动方式)进行了工具,并且通过寻找峰值和可能的模式研究了异常。我们已经在Python中创建了一个框架,以表示和研究时间序列并分析数据和绘图图。该项目已上传到Github上。我们的分析强调了大气压力的移动平均值与住院数量的移动平均值之间存在很强的相关性(r = -0.9468,p <0,001),尽管因果关系仍然未知。还检测到短期到中期的时间以来,以大量半小时的压力变化为特征,住院数量增加。通过傅立叶变换获得的频谱图需要增加数据集。分析的数据(尤其是住院数据)太少,无法进行这种分析。

Pulmonary embolism (PE) and deep vein thrombosis (DVT) are gathered in venous thromboembolism (VTE) and represent the third cause of cardiovascular diseases. Recent studies suggest that meteorological parameters as atmospheric pressure, temperature, and humidity could affect PE incidence but, nowadays, the relationship between these two phenomena is debated and the evidence is not completely explained. The clinical experience of the Department of Emergency Medicine at AOUC Hospital suggests the possibility that a relationship effectively exists. We have collected data concerning the Emergency Medicine Unit admissions of PE patients to confirm our hypothesis. At the same time, atmospheric parameters are collected from the Lamma Consortium of Tuscany region. We have implemented new IT models and statistic tools by using semi-hourly records of weather time high resolution data to process the dataset. We have carried out tools from econometrics, like mobile means, and we have studied anomalies through the search for peaks and possible patterns. We have created a framework in Python to represent and study time series and to analyze data and plot graphs. The project has been uploaded on GitHub. Our analyses highlighted a strong correlation between the moving averages of atmospheric pressure and those of the hospitalizations number (R= -0.9468, p<0,001) although causality is still unknown. The existence of an increase in the number of hospitalizations in the days following short-to-medium periods of time characterized by a high number of half-hourly pressure changes is also detected. The spectrograms studies obtained by the Fourier transform requires to increase the dataset. The analyzed data (especially hospitalization data) were too few to carry out this kind of analyses.

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