一个模型,用于确定花粉计数使用谷歌搜索查询患者过敏性鼻炎。

Journal of allergy Pub Date : 2014-01-01 Epub Date: 2014-06-19 DOI:10.1155/2014/381983
Volker König, Ralph Mösges
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引用次数: 20

摘要

背景。花粉相关过敏的跨区域增加及其多样性已得到科学证明。然而,在许多地区,斑片花粉计数测量是一个世界性的问题,只有少数例外。方法。本文使用从德国花粉计数站收集的数据,使用相关过敏学/生物学关键词的谷歌查询,以及来自三个德国研究中心的患者数据,这些数据来自一项前瞻性,双盲,随机,安慰剂对照,多中心免疫治疗研究,以分析这些数据池之间可能的相关性。结果。总体而言,基于患者的综合症状药物评分与谷歌数据之间的相关性强于区域测量花粉计数数据。谷歌数据的相关性在严重过敏患者群体中尤为明显。三中心分析的结果显示,在11组(3个平均患者队列和8个严重过敏患者亚组:高IgE级、高综合症状药物评分和哮喘)中的10组中,与Google关键词有中等到强的相关性(交叉相关系数高达>0.8,P < 0.001)。结论。对于那些拥有良好互联网基础设施但没有密集花粉陷阱网络的国家来说,这可能是确定花粉水平和预测第二天花粉数量的另一种选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A model for the determination of pollen count using google search queries for patients suffering from allergic rhinitis.

Background. The transregional increase in pollen-associated allergies and their diversity have been scientifically proven. However, patchy pollen count measurement in many regions is a worldwide problem with few exceptions. Methods. This paper used data gathered from pollen count stations in Germany, Google queries using relevant allergological/biological keywords, and patient data from three German study centres collected in a prospective, double-blind, randomised, placebo-controlled, multicentre immunotherapy study to analyse a possible correlation between these data pools. Results. Overall, correlations between the patient-based, combined symptom medication score and Google data were stronger than those with the regionally measured pollen count data. The correlation of the Google data was especially strong in the groups of severe allergy sufferers. The results of the three-centre analyses show moderate to strong correlations with the Google keywords (up to >0.8 cross-correlation coefficient, P < 0.001) in 10 out of 11 groups (three averaged patient cohorts and eight subgroups of severe allergy sufferers: high IgE class, high combined symptom medication score, and asthma). Conclusion. For countries with a good Internet infrastructure but no dense network of pollen traps, this could represent an alternative for determining pollen levels and, forecasting the pollen count for the next day.

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