An AI approach to COVID-19 infection risk assessment in virtual visits: a case report.

OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for COVID-19 testing, we used natural language processing (NLP) and artificial intelligence (AI)-based methods with unstructured patient data collected through telehealth visits. 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 AUC of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. DISCUSSION: Informatics tools such as NLP and AI methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.