| Autor: |
Eduardo Higa, Abir Elbéji, Lu Zhang, Aurélie Fischer, Gloria A Aguayo, Petr V Nazarov, Guy Fagherazzi |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
JMIR Medical Informatics, Vol 10, Iss 11, p e35622 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2291-9694 |
| DOI: |
10.2196/35622 |
| Popis: |
BackgroundThe COVID-19 disease has multiple symptoms, with anosmia and ageusia being the most prevalent, varying from 75% to 95% and from 50% to 80% of infected patients, respectively. An automatic assessment tool for these symptoms will help monitor the disease in a fast and noninvasive manner. ObjectiveWe hypothesized that people with COVID-19 experiencing anosmia and ageusia had different voice features than those without such symptoms. Our objective was to develop an artificial intelligence pipeline to identify and internally validate a vocal biomarker of these symptoms for remotely monitoring them. MethodsThis study used population-based data. Participants were assessed daily through a web-based questionnaire and asked to register 2 different types of voice recordings. They were adults (aged >18 years) who were confirmed by a polymerase chain reaction test to be positive for COVID-19 in Luxembourg and met the inclusion criteria. Statistical methods such as recursive feature elimination for dimensionality reduction, multiple statistical learning methods, and hypothesis tests were used throughout this study. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Prediction Model Development checklist was used to structure the research. ResultsThis study included 259 participants. Younger (aged |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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