An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report

Abstract

Objective: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits.

Materials and methods: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.

Results: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.

Conclusions: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.

Keywords: AI; COVID-19; artificial intelligence; risk assessment; text analytics.

Figure 1.

Top 10 words that are overrepresented in patients who tested positive for COVID-19 (coronavirus disease 2019), showing relevant words expressed by patients during the virtual care visit intake process.

Figure 2.

The area under the receiver-operating characteristic curve (AUC) of the convolutional neural network for predicting SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive results based on the text content of the virtual care visit notes.