SavedAdd your journal

Journals / Middle East Journal of Science / 2021 / Cilt: 7 - Sayı: 2

TIME SERIES OUTLIER ANALYSIS FOR MODEL, DATA AND HUMAN-INDUCED RISKS IN COVID-19 SYMPTOMS DETECTION

Ahmet KAYA
Rojan GÜMÜŞ
ÖMER AYDIN
Pages
123–136
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

With the method revealed here, outliers originating from healthcare workers or test apparatus can be detected and more accurate results can be obtained by replacing these outliers with estimated values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Information systems are important references aiming to support the decisions of decision-makers.
📊
Key Findings & Data:It is important to identify and extract outliers in decision support systems developed for the health information systems such as the detection system of Covid-19 symptoms.
💡
Conclusion & Impact:With the method revealed here, outliers originating from healthcare workers or test apparatus can be detected and more accurate results can be obtained by replacing these outliers with estimated values.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Covid-19 · Health information systems · time series

Abstract

Information systems are important references aiming to support the decisions of decision-makers. Information reliability depends on the accuracy and efficacy of data and models. Therefore, some risks may emerge in information systems concerning models, data and humans. It is important to identify and extract outliers in decision support systems developed for the health information systems such as the detection system of Covid-19 symptoms. In this study, the risks that are important in decision making in Covid-19 symptom detection were determined by the statistical time series (ARMA) approach. Potential solutions are proposed in this way. Moreover, outliers are detected by software developed by using the Box-Jenkins model and reliability and accuracy of data is increased by using estimated data instead of outliers. In the implementation of this study, time-series-based data obtained from laboratory examinations of Covid-19 test devices can be used. With the method revealed here, outliers originating from healthcare workers or test apparatus can be detected and more accurate results can be obtained by replacing these outliers with estimated values.

🔖

Cite This Article

KAYA, A., GÜMÜŞ, R., AYDIN, Ö. (2021). TIME SERIES OUTLIER ANALYSIS FOR MODEL, DATA AND HUMAN-INDUCED RISKS IN COVID-19 SYMPTOMS DETECTION. Middle East Journal of Science, Cilt: 7 - Sayı: 2, 123–136. https://ajindex.com/articles/408948

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Akouemo, H., Povinelli, R., “Data Improving in Time Series Using ARX and ANN Models”. IEEE PES Transaction on Power Systems. 32, 3352-3359, 2015.
  2. Ergun, Ü., “Modern Management Accounting Applications by Information Technologies” Dokuz Eylul University Journal of Faculty of Economics and Administrative Sciences, 11, 1-17, 1995.
  3. Soyuer, H., İşletmelerde Bilgisayar Destekli Bilgi Sistemi Uygulamaları ve Üretim/İşlemler Yönetiminde Bilgisayara Dayalı Sistemler, Ph. D. thesis, Gazi University Social Sciences Institute, Ankara, TR, 2000 O’Brein, J.A., Introduction to Information Systems. Irwin McGraw Hill, Boston, 1997
  4. Earl, M.J., “Experiences in Strategic Information System Planning”. MIS Quarterly. 17(1), 1-12, 1993.
  5. Kalıpsız, O., Buharalı, A., Biricik, G., Sistem Analizi ve Tasarımı. [System Analysis and Design], Papatya Yayıncılık Eğitim, İstanbul, TR, 2011
  6. Lucas, H., Information System Concept for Management (5th Edition), McGraw-Hill, New York, NY, 1994
  7. Peppard, J., IT strategy for Business, Pitman Publishing, New York, NY, 1993
  8. Katsikas, S.K., “Health Care Management and Information Systems Security: awareness, training, or education?”, International Journal of Medical Informatics, 60, 129-135, 2000.
  9. Gritsalis, D.A., “Enhancing Security and Improving Interoperability in Healthcare Information Systems”, Medical Informatics, 23(4), 309-324, 1998.
  10. Ward, M.J., Self, W.H., Froehle, C.M., “Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation”, Academic emergency medicine: official journal of the Society for Academic Emergency Medicine, 22(9), 1085-1092, 2015.
  11. Kaya, A., “Modelling for Detection Processes Outliers in Time Series and Its Performance Analysis”, Uludag University Journal of Faculty of Economics and Administrative Sciences, 22 (1), 271-279, 2003.
  12. Chang, I., Tiao, G.C., Chen, C., “Estimation of Time Series Parameters in the Presence of Outliers”, Technometrics, 30(2),193-204, 1988. Doi:10.1080/00401706.1988.10488367 Andrews, D., Pregibon, D., “Finding the Outliers that Matter. Journal of the Royal Statistical Society”, Series B (Methodological), 40(1), 85-93, 1978.
  13. Fox, A.J., “Outliers in Time Series”. Journal of the Royal Statistical Society Series B, 34, 350-363, 1972. Hillmer. S.C., “Monitoring and Adjusting Forecasts in the Presence of Additive Outliers”, Journal of Forecasting, 3, 205-221, 1984.
  14. Tsay, R.S., “Time Series Model Specification in the Presence of O