Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections—Upper Silesia pilot study

Mika, Justyna; Tobiasz, Joanna; Żyła, Joanna; Papież, Anna; Bach, Małgorzata; Werner, A.; Kozielski, Michał; Kania, Mateusz; Gruca, Aleksandra; Piotrowski, Damian; Sobala-Szczygieł, Barbara; Włostowska, Bożena; Foszner, Paweł; Sikora, Marek; Polańska, Joanna; Jaroszewicz, Jerzy

doi:10.1038/s41598-021-93046-6

Cited by 9 publications

(13 citation statements)

References 48 publications

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“…Their local inflammation becomes exacerbated, possibly progressing to an acute respiratory distress syndrome (ARDS) [30]. Overall, the course of this infection can vary between asymptomatic (no symptoms), mild (recurrent respiratory and non-respiratory symptoms), moderate (pneumonia-like symptoms), and severe (acute pneumonia) [31].…”

Section: Scientific Background a Covid-19 Biological Impact On Respir...mentioning

confidence: 99%

Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

et al. 2022

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As long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound-based analysis of vocal and respiratory audio data to achieve that objective. This work presents a modular data-centric Machine Learning pipeline for COVID-19 identification from voice and respiratory audio samples. Signals are processed to extract and classify relevant segments that contain informative events, such as coughing or breathing. Temporal, amplitude, spectral, cepstral, and phonetic features are extracted from audio along with available metadata for COVID-19 identification. Audio augmentation and data balancing techniques are used to mitigate class disproportionality. The open-access Coswara and COVID-19 Sounds datasets were used to test the performance of the proposed architecture. Obtained sensitivity scores ranged from 60.00% to 80.00% in Coswara and from 51.43% to 77.14% in COVID-19 Sounds. Although previous works report higher accuracy on COVID-19 detection, this research focused on a data-centric approach by validating the quality of the samples, segmenting the speech events, and exploring interpretable features with physiological meaning. As the pandemic evolves, its lessons must endure, and pipelines such as the proposed one will help prepare new stages where quick and easy disease identification is essential.

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Section: Scientific Background a Covid-19 Biological Impact On Respir...mentioning

confidence: 99%

Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

et al. 2022

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“…In this sense, various predicting rules have been proposed that combine multiple individual features (age, gender, symptoms, demographic data, etc. ), to characterize COVID-19 infected people [6,7,8,9,10,11,12,13,14,15,16,17,18,19]. These detection methods typically rely on the presence of the most predictive combination of symptoms in order to identify a COVID-19 active case.…”

Section: Introductionmentioning

confidence: 99%

Consistent comparison of symptom-based methods for COVID-19 infection detection

Rufino

Ramirez

Aguilar

et al. 2023

International Journal of Medical Informatics

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Section: Introductionmentioning

confidence: 99%

Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection

Rufino

Baquero

Champati

et al. 2022

Preprint

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Multiple COVID-19 diagnosis methods based on information collected from patients have been proposed during the global pandemic crisis, with the aim of providing medical staff with quick diagnosis tools to efficiently plan and manage the limited healthcare resources. In general, these methods have been developed to detect COVID-19 positive cases from a particular combination of reported symptoms, and have been evaluated using datasets extracted from different studies with different characteristics. On the other hand, the University of Maryland, in partnership with Facebook, launched the Global COVID-19 Trends and Impact Survey (UMD-CTIS), the largest health surveillance tool to date that has collected information from 114 countries/territories since April 2020. This survey captured various individual features including gender, age groups, self-reported symptoms, isolation measures, and mental health status, among others. In this paper, we compare the performance of different proposed COVID-19 diagnosis methods using the information collected by UMD-CTIS, for the years 2020 and 2021, in five countries: Brazil, Canada, Germany, Japan, and South Africa. The evaluation of these methods with homogeneous data across countries and years provides a solid and consistent comparison among them.

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Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections—Upper Silesia pilot study

Cited by 9 publications

References 48 publications

Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

Clinically Relevant Sound-Based Features in COVID-19 Identification: Robustness Assessment With a Data-Centric Machine Learning Pipeline

Consistent comparison of symptom-based methods for COVID-19 infection detection

Consistent Comparison of Symptom-based Methods for COVID-19 Infection Detection

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