TY - JOUR AU - Neuraz, Antoine AU - Lerner, Ivan AU - Digan, William AU - Paris, Nicolas AU - Tsopra, Rosy AU - Rogier, Alice AU - Baudoin, David AU - Cohen, Kevin Bretonnel AU - Burgun, Anita AU - Garcelon, Nicolas AU - Rance, Bastien PY - 2020 DA - 2020/8/14 TI -基于自然语言处理的突发疾病快速响应:钙通道阻滞剂和高血压在COVID-19大流行中的案例研究JO - J Med Internet Res SP - e20773 VL - 22 IS - 8 KW -药物信息KW -自然语言处理KW -电子健康记录KW - COVID-19 KW -公共卫生KW -应对KW -突发疾病KW -信息学AB -背景:一种新型疾病对信息学解决方案提出了特殊挑战。生物医学信息学在很大程度上依赖于结构化数据，这需要预先存在的数据或知识模型;然而，新型疾病没有预先存在的知识模型。在一种新兴的流行病中，语言处理可以使非结构化文本快速转换为新的知识模型。然而，尽管这个想法经常被提出，却没有机会进行实际的实时测试。当前的冠状病毒病(COVID-19)大流行提供了这样一个机会。目的:本研究的目的是利用自然语言处理(NLP)评估临床文本信息在应对突发疾病中的附加价值。方法:我们利用两种信息来源:严格从结构化电子病历(EHRs)获得的数据和通过结构化电子病历和文本挖掘获得的数据，探讨钙通道阻滞剂长期治疗对住院期间高血压患者COVID-19感染结局的影响。结果:在这项涉及39家医院的多中心研究中，文本挖掘提高了统计能力，足以将调整后的风险比的负结果变为正结果。 Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions: In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable. SN - 1438-8871 UR - //www.mybigtv.com/2020/8/e20773/ UR - https://doi.org/10.2196/20773 UR - http://www.ncbi.nlm.nih.gov/pubmed/32759101 DO - 10.2196/20773 ID - info:doi/10.2196/20773 ER -