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An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.
Obeid, Jihad S; Davis, Matthew; Turner, Matthew; Meystre, Stephane M; Heider, Paul M; O'Bryan, Edward C; Lenert, Leslie A.
Afiliación
  • Obeid JS; Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Davis M; Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Turner M; Information Solutions, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Meystre SM; Information Solutions, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Heider PM; Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.
  • O'Bryan EC; Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.
  • Lenert LA; Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA.
J Am Med Inform Assoc ; 27(8): 1321-1325, 2020 08 01.
Article en En | MEDLINE | ID: mdl-32449766
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía Viral / Procesamiento de Lenguaje Natural / Inteligencia Artificial / Telemedicina / Infecciones por Coronavirus Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía Viral / Procesamiento de Lenguaje Natural / Inteligencia Artificial / Telemedicina / Infecciones por Coronavirus Tipo de estudio: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: J Am Med Inform Assoc Asunto de la revista: INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido