@文章{info:doi/10.2196/36119，作者="Caskey, John和McConnell, Iain L和Oguss, Madeline和Dligach, Dmitriy和Kulikoff, Rachel和Grogan, Brittany和Gibson, Crystal和Wimmer, Elizabeth和DeSalvo, Traci E和nyake - nyasani, Edwin E和Churpek, Matthew M和Afshar, Majid"，标题="从公共卫生部门的接触者追踪访谈表格中识别COVID-19暴发:自然语言处理管道的开发"，期刊="JMIR公共卫生监测"，年="2022"，月="3"，日="8"，卷="8"，数="3"，页="e36119"，关键词="自然语言处理;公共卫生信息学;命名实体识别;接触者追踪;COVID-19;爆发;神经语言模型;疾病监测;数字健康; electronic surveillance; public health; digital surveillance tool", abstract="Background: In Wisconsin, COVID-19 case interview forms contain free-text fields that need to be mined to identify potential outbreaks for targeted policy making. We developed an automated pipeline to ingest the free text into a pretrained neural language model to identify businesses and facilities as outbreaks. Objective: We aimed to examine the precision and recall of our natural language processing pipeline against existing outbreaks and potentially new clusters. Methods: Data on cases of COVID-19 were extracted from the Wisconsin Electronic Disease Surveillance System (WEDSS) for Dane County between July 1, 2020, and June 30, 2021. Features from the case interview forms were fed into a Bidirectional Encoder Representations from Transformers (BERT) model that was fine-tuned for named entity recognition (NER). We also developed a novel location-mapping tool to provide addresses for relevant NER. Precision and recall were measured against manually verified outbreaks and valid addresses in WEDSS. Results: There were 46,798 cases of COVID-19, with 4,183,273 total BERT tokens and 15,051 unique tokens. The recall and precision of the NER tool were 0.67 (95{\%} CI 0.66-0.68) and 0.55 (95{\%} CI 0.54-0.57), respectively. For the location-mapping tool, the recall and precision were 0.93 (95{\%} CI 0.92-0.95) and 0.93 (95{\%} CI 0.92-0.95), respectively. Across monthly intervals, the NER tool identified more potential clusters than were verified in WEDSS. Conclusions: We developed a novel pipeline of tools that identified existing outbreaks and novel clusters with associated addresses. Our pipeline ingests data from a statewide database and may be deployed to assist local health departments for targeted interventions. ", issn="2369-2960", doi="10.2196/36119", url="https://publichealth.www.mybigtv.com/2022/3/e36119", url="https://doi.org/10.2196/36119", url="http://www.ncbi.nlm.nih.gov/pubmed/35144241" }