ArticleJournal of clinical medicine2021
Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Taste loss as a distinct symptom of COVID-19: a systematic review and meta-analysis.Chemical senses · 2023Pooled it
- The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.International journal of medical informatics · 2022Pooled it
- Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study.BMC infectious diseases · 2022Pooled it
- A COVID-19 prediction Model based on symptomatology google trends and its optimization.BMC infectious diseases · 2026Article
- Machine learning predictive modeling of the persistence of post-Covid19 disorders: Loss of smell and taste as case studies.Heliyon · 2024Article
- A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging.Heliyon · 2024Article
- The Value of Subjective Olfactometry as a Predictive Biomarker of Neurodegenerative Diseases: A Systematic Review.Life (Basel, Switzerland) · 2024Review
- Symptom-based scoring technique by machine learning to predict COVID-19: a validation study.BMC infectious diseases · 2023Article
- Machine Learning-Based Prediction of COVID-19 Prognosis Using Clinical and Hematologic Data.Cureus · 2023Article
- Transient loss and recovery of oral chemesthesis, taste and smell with COVID-19: A small case-control series.Physiology & behavior · 2023Article
- The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Review
- Towards an ML-based semantic IoT for pandemic management: A survey of enabling technologies for COVID-19.Neurocomputing · 2023Article
- Transient loss and recovery of oral chemesthesis, taste and smell with COVID-19: a small case-control series.medRxiv : the preprint server for health sciences · 2023Article
- Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives.Array (New York, N.Y.) · 2023Review
- Associations of Country-Specific and Sociodemographic Factors With Self-Reported COVID-19-Related Symptoms: Multivariable Analysis of Data From the CoronaCheck Mobile Health Platform.JMIR public health and surveillance · 2023Article
- A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN.European journal of medical research · 2023Article
- Compression-Complexity Measures for Analysis and Classification of Coronaviruses.Entropy (Basel, Switzerland) · 2022Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
- Potential pitfalls in analysing a SARS-CoV-2 RT-PCR assay and how to standardise data interpretation.Journal of virological methods · 2022Article
- Analysis of Prevalence and Predictive Factors of Long-Lasting Olfactory and Gustatory Dysfunction in COVID-19 Patients.Life (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.
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