BACKGROUND Reported data regarding the relation between the incidence of spontaneous subarachnoid hemorrhage (SAH) and weather conditions are conflicting and do so far not allow prognostic models. METHODS Admissions for spontaneous SAH (ICD I60.*) 2009-2018 were retrieved form our hospital data base. Historical meteorological data for the nearest meteorological station, Düsseldorf Airport, was retrieved from the archive of the Deutsche Wetterdienst (DWD). Airport is in the center of our catchment area with a diameter of approximately 100 km. Pearson correlation matrix between mean daily meteorological variables and the daily admissions of one or more patients with subarachnoid hemorrhage was calculated and further analysis was done using deep learning algorithms. RESULTS For the 10-year period from January 1, 2009 until December 31, 2018, a total of 1569 patients with SAH were admitted. No SAH was admitted on 2400 days (65.7%), 1 SAH on 979 days (26.7%), 2 cases on 233 days (6.4%), 3 SAH on 37 days (1.0%), 4 in 2 days (0.05%), and 5 cases on 1 day (0.03%). Pearson correlation matrix suggested a weak positive correlation of admissions for SAH with precipitation on the previous day and weak inverse relations with the actual mean daily temperature and the temperature change from the previous days, and weak inverse correlations with barometric pressure on the index day and the day before. Clustering with admission of multiple SAH on a given day followed a Poisson distribution and was therefore coincidental. The deep learning algorithms achieved an area under curve (AUC) score of approximately 52%. The small difference from 50% appears to reflect the size of the meteorological impact. CONCLUSION Although in our data set a weak correlation of the probability to admit one or more cases of SAH with meteorological conditions was present during the analyzed time period, no helpful prognostic model could be deduced with current state machine learning methods. The meteorological influence on the admission of SAH appeared to be in the range of only a few percent compared with random or unknown factors.

中文翻译：

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杜塞尔多夫大区蛛网膜下腔出血的气象因素被重新研究：一种机器学习方法，可预测蛛网膜下腔出血患者的入院可能性。

背景技术关于自发性蛛网膜下腔出血（SAH）的发生率与天气状况之间的关系的报道数据存在矛盾，到目前为止，尚无预后模型。方法从我们的医院数据库中检索自发性SAH（ICD I60。*）2009-2018年的入院信息。最近的气象站杜塞尔多夫机场的历史气象数据已从Deutsche Wetterdienst（DWD）的档案中获取。机场位于我们集水区的中心，直径约100公里。计算平均每日气象变量与一名或多名蛛网膜下腔出血患者每日住院量之间的皮尔逊相关矩阵，并使用深度学习算法进行进一步分析。结果从2009年1月1日至2018年12月31日的10年期间，总共接纳了1569名SAH患者。2400天（65.7％）未接受SAH，979天（16.7％）接受1次SAH，233天（6.4％）接受2次SAH，37天（1.0％）接受3次SAH，2天接受4次（0.05％） ，以及1天5例（0.03％）。皮尔逊相关矩阵表明，SAH的入场与前一天的降水呈弱正相关，与实际平均日温度和前几天的温度变化呈弱逆相关，在指数日和大气层与大气压力呈弱逆相关。前一天。在给定的一天内接受多个SAH的聚类遵循泊松分布，因此是偶然的。深度学习算法获得的曲线下面积（AUC）得分约为52％。与50％的微小差异似乎反映出气象影响的大小。结论尽管在我们的数据集中，在分析的时间段内，接纳一例或多例SAH与气象条件的可能性之间存在弱相关性，但当前状态机学习方法无法推论有用的预后模型。与随机或未知因素相比，气象对SAH进入的影响似乎只有百分之几。