Details
Title
Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisationJournal title
Bulletin of the Polish Academy of Sciences Technical SciencesYearbook
2021Volume
69Issue
4Authors
Affiliation
Kozielski, Michał : Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland ; Henzel, Joanna : Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland ; Tobiasz, Joanna : Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland ; Gruca, Aleksandra : Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland ; Foszner, Paweł : Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland ; Zyla, Joanna : Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland ; Bach, Małgorzata : Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland ; Werner, Aleksandra : Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland ; Jaroszewicz, Jerzy : Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland ; Polańska, Joanna : Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland ; Sikora, Marek : Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, PolandKeywords
classification ; classification quality ; screening ; COVID-19Divisions of PAS
Nauki TechniczneCoverage
e137349Bibliography
- L.D. Maxim, R. Niebo, and M.J. Utell, “Screening tests: a review with examples”, Inhal. Toxicol., vol. 26, no. 13, pp. 811–828, 2014.
- D. Ardila et al., “End-to-end lung cancer screening with threedimensional deep learning on low-dose chest computed tomography”, Nat. Med., vol. 25, no. 6, pp. 954–961, 2019.
- R. Landy, F. Pesola, A. Castañón, and P. Sasieni, “Impact of cervical screening on cervical cancer mortality: estimation using stage-specific results from a nested case–control study”, Br. J. Cancer, vol. 115, no. 9, pp. 1140–1146, 2016.
- E.F. Conant et al., “Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the prospr consortium”, Breast Cancer Res. Treat., vol. 156, no. 1, pp. 109–116, 2016.
- K. Gostic, A.C. Gomez, R.O. Mummah, A.J. Kucharski, and J.O. Lloyd-Smith, “Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19”, eLife 9, vol. 9, p. e55570, 2020.
- L. Wynants et al., “Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal”, BMJ, vol. 369, p. m1328, 2020.
- A. Callahan et al., “Estimating the efficacy of symptom-based screening for COVID-19”, NPJ Digit. Med., vol. 3, no. 1, pp. 1–3, 2020.
- R. Trevethan, “Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice”, Front. Public Health, vol. 5, p. 307, 2017.
- J. Henzel et al., “Classification supporting COVID-19 diagnostics based on patient survey data”, arXiv:2011.12247, 2020.
- H. Swapnarekha, H.S. Behera, J. Nayak, and B. Naik, “Role of intelligent computing in COVID-19 prognosis: A state-of-theart review”, Chaos Solitons Fractals, vol. 138, p. 109947, 2020.
- S. Lalmuanawma, J. Hussain, and L. Chhakchhuak, “Applications of machine learning and artificial intelligence for COVID-19 (SARS- CoV-2) pandemic: A review”, Chaos Solitons Fractals, vol. 139, p. 110059, 2020.
- Y. Mohamadou, A. Halidou, and P.T. Kapen, “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19”, Appl. Intell., vol. 50, no. 11, pp. 3913–3925, 2020.
- A. Banerjee et al., “Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population”, Int. Immunopharmacol., vol. 86, p. 106705, 2020.
- T. Ozturk, M. Talo, E.A. Yildirim, U.B. Baloglu, O. Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with x-ray images”, Comput. Biol. Med., vol. 121, p. 103792, 2020.
- R.M. Pereira, D. Bertolini, L.O. Teixeira, C.N. Silla, and Y.M. Costa, “COVID-19 identification in chest x-ray images on flat and hierarchical classification scenarios”, Comput. Meth. Programs Biomed., vol. 194, p. 105532, 2020.
- D. Singh, V. Kumar, V. Yadav, and M. Kaur, “Deep neural network-based screening model for COVID-19-infected patients using chest x-ray images”, Int. J. Pattern Recogn. Artif. Intell., vol. 25, no. 3, p. 2151004, 2021.
- A.G. Wintjens et al., “Applying the electronic nose for preoperative SARS-CoV-2 screening”, Surg. Endosc., 2020, doi: 10.1007/s00464- 020-08169-0.
- J. Laguarta, F. Hueto, and B. Subirana, “COVID-19 artificial intelligence diagnosis using only cough recordings”, IEEE Eng. Med. Biol. Mag., vol. 1, pp. 275‒281, 2020.
- P. Bagad et al., “Cough against covid: Evidence of covid-19 signature in cough sounds”, arXiv:2009.08790 (2020).
- C. Feng et al., “A novel triage tool of artificial intelligence assisted diagnosis aid system for suspected COVID-19 pneumonia in fever clinics”, medRxiv 2020.03.19.20039099.
- D. Brinati, A. Campagner, D. Ferrari, M. Locatelli, G. Banfi, and F. Cabitza, “Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study”, J. Med. Syst., vol. 44, p. 135, 2020.
- “Suspected COVID-19 pneumonia diagnosis aid system”, [Online]. Available: https://intensivecare.shinyapps.io/COVID19/, (Accessed on 28/12/2020).
- “ML-based COVID-19 test from routine blood test”, [Online]. Available: https://covid19-blood-ml.herokuapp.com/, (Accessed on 28/12/2020).
- Symptomate, “Symptomate COVID-19 risk assessment tool”, [Online]. Available: https://symptomate.com/covid19/checkup, (Accessed on 12/28/2020).
- CDC, “Testing for COVID-19”, [Online]. Available: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/testing.html, (Accessed on 28/12/2020).
- Apple Inc., “Apple covid-19”, [Online]. Available: https://covid19.apple.com/screening, (Accessed on 28/12/2020).
- “COVID-19 Risk Assessment”, [Online]. Available: https://covid.preflet.com/en, (Accessed on 28/12/2020).
- M. DataLab, M. Groups, “COVID-19 risk calculator”. [Online]. Available: https://crs19.pl/ (Accessed on 28/12/2020).
- P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed. Chapman & Hall, 1989.
- T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
- “Open source machine learning platform”. [Online]. Available: https://www.h2o.ai/, (Accessed on 20/12/2020).
- “Data drivEn COVID19 DEtection with machine learning”). [Online]. Available: https://decode.polsl.pl (Accessed on 30/12/2020).