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Infection prediction using physiological and social data in social environments

机译：在社交环境中使用生理和社交数据进行感染预测

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The ability of detecting infections at an early stage in clinical environments is an important clinical problem. When an infection is not diagnosed on time, it may not only affect the health of the infected patient, but also spread and infect other people. In this paper, we propose the development of a clinical decision support system (CDSS) for diagnosing infections using clinical signals from patients. This system is designed to be able to cope with small amounts of data (a single record per day and patient), making it convenient for environments under strict constraints (such as low resources or bad connectivity). Additionally, we have incorporated data from external sources, in order to enrich the quality of the models. In particular, we have considered social data arising from web searches, retrieved from Google Trends, as well as weather data. Clinical data was recorded between April 2018 and July 2019 in two nursing homes in Spain and one in Dominican Republic, where nurses had also tested patients for infections. Feature extraction was carried out by aggregating measurements from days before to the infection (lead) and after the infection was detected (lag), and these features were used to train supervised learning models. The best model attained using only clinical data attains an AUROC of 0.734. When data is enriched with external sources, this performance increases up to an AUROC of 0.798. In the case of prognosis (i.e., only measurements before the manual annotation of the infection are used) an AUROC of 0.719 is obtained using only clinical data, and up to 0.757 when combining additional sources of data. In conclusion, the CDSS provides a good recognition performance given the small amounts of data available. This performance can be increased by including social data, which are readily available, and can therefore be useful in scenarios where clinical data acquisition is expensive or unfeasible.
机译：在临床环境中早期检测感染的能力是重要的临床问题。如果不能及时诊断出感染，不仅会影响被感染患者的健康，还会传播并感染其他人。在本文中，我们建议开发一种临床决策支持系统（CDSS），用于使用来自患者的临床信号诊断感染。该系统旨在处理少量数据（每天和患者每天一条记录），使其在严格限制（例如资源不足或连接不良）下的环境中使用十分方便。此外，我们合并了来自外部来源的数据，以丰富模型的质量。特别是，我们考虑了从Google趋势检索到的网络搜索产生的社交数据以及天气数据。在2018年4月至2019年7月期间，在西班牙的两家疗养院和多米尼加共和国的一家疗养院记录了临床数据，那里的护士还对患者进行了感染检查。通过汇总从感染之前到感染前数天（铅）和检测到感染后（滞后）的测量值进行特征提取，并将这些特征用于训练监督学习模型。仅使用临床数据获得的最佳模型的AUROC为0.734。当数据富含外部来源时，此性能将提高到0.798的AUROC。在预后的情况下（即仅使用手动标记感染前的测量），仅使用临床数据即可获得0.719的AUROC，而在组合其他数据源时最高可得到0.757。总之，鉴于可用数据量少，CDSS提供了良好的识别性能。通过包含易于获得的社交数据，可以提高此性能，因此在临床数据获取昂贵或不可行的情况下很有用。

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