An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.
J Am Med Inform Assoc
; 27(8): 1321-1325, 2020 08 01.
Article
in English
| MEDLINE | ID: covidwho-629242
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 ANDMETHODS:
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Natural Language Processing
/
Artificial Intelligence
/
Telemedicine
/
Coronavirus Infections
Type of study:
Case report
/
Diagnostic study
/
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
J Am Med Inform Assoc
Journal subject:
Medical Informatics
Year:
2020
Document Type:
Article
Affiliation country:
Jamia
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