Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline.
JMIR Public Health Surveill
; 8(3): e36119, 2022 03 08.
Article
em En
| MEDLINE
| ID: mdl-35144241
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Linguagem Natural
/
COVID-19
Tipo de estudo:
Prognostic_studies
/
Qualitative_research
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article