Seol Hee Yun, Sohn Sunghwan, Liu Hongfang, Wi Chung-Il, Ryu Euijung, Park Miguel A, Juhn Young J
Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN, United States.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
Front Pediatr. 2019 Apr 2;7:113. doi: 10.3389/fped.2019.00113. eCollection 2019.
Emerging literature suggests that delayed identification of childhood asthma results in an increased risk of long-term and various morbidities compared to those with timely diagnosis and intervention, and yet this risk is still overlooked. Even when children and adolescents have a history of recurrent asthma-like symptoms and risk factors embedded in their medical records, this information is sometimes overlooked by clinicians at the point of care. Given the rapid adoption of electronic health record (EHR) systems, early identification of childhood asthma can be achieved utilizing (1) asthma ascertainment criteria leveraging relevant clinical information embedded in EHR and (2) innovative informatics approaches such as natural language processing (NLP) algorithms for asthma ascertainment criteria to enable such a strategy. In this review, we discuss literature relevant to this topic and introduce recently published informatics algorithms (criteria-based NLP) as a potential solution to address the current challenge of early identification of childhood asthma.
新出现的文献表明,与那些得到及时诊断和干预的儿童相比,儿童哮喘的延迟识别会增加长期及各种疾病的风险,然而这种风险仍然被忽视。即使儿童和青少年有复发性哮喘样症状的病史以及病历中记载的风险因素,这些信息有时在医疗护理时也会被临床医生忽视。鉴于电子健康记录(EHR)系统的迅速采用,可以利用以下方法实现儿童哮喘的早期识别:(1)利用EHR中嵌入的相关临床信息的哮喘判定标准,以及(2)创新的信息学方法,如用于哮喘判定标准的自然语言处理(NLP)算法,以实现这样的策略。在这篇综述中,我们讨论了与该主题相关的文献,并介绍了最近发表的信息学算法(基于标准的NLP),作为应对当前儿童哮喘早期识别挑战的潜在解决方案。