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

Year 2021, Volume: 7 Issue: 2, 123 - 136, 30.12.2021

Ahmet Kaya , Rojan Gümüş , Ömer Aydın

https://doi.org/10.51477/mejs.970510

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.

Keywords

Covid-19, Health information systems, time series, outlier analysis, ARMA

References

• Akouemo, H., Povinelli, R., “Data Improving in Time Series Using ARX and ANN Models”. IEEE PES Transaction on Power Systems. 32, 3352-3359, 2015.
• Ergun, Ü., “Modern Management Accounting Applications by Information Technologies” Dokuz Eylul University Journal of Faculty of Economics and Administrative Sciences, 11, 1-17, 1995.
• 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
• Earl, M.J., “Experiences in Strategic Information System Planning”. MIS Quarterly. 17(1), 1-12, 1993.
• Kalıpsız, O., Buharalı, A., Biricik, G., Sistem Analizi ve Tasarımı. [System Analysis and Design], Papatya Yayıncılık Eğitim, İstanbul, TR, 2011
• Lucas, H., Information System Concept for Management (5th Edition), McGraw-Hill, New York, NY, 1994
• Peppard, J., IT strategy for Business, Pitman Publishing, New York, NY, 1993
• Katsikas, S.K., “Health Care Management and Information Systems Security: awareness, training, or education?”, International Journal of Medical Informatics, 60, 129-135, 2000.
• Gritsalis, D.A., “Enhancing Security and Improving Interoperability in Healthcare Information Systems”, Medical Informatics, 23(4), 309-324, 1998.
• 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.
• 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.
• 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.
• 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.
• Tsay, R.S., “Time Series Model Specification in the Presence of Outliers”, Journal of the American Statistical Association, 81(393), 132-141, 1986.
• Pena, D., Measuring the importance of outliers in ARIMA models. New Perspectives in Theoretical and Applied Statistics John Wiley, New York, USA, 1987
• Abraham, B., Yatawara, N. A., “Score Test for Detection of Time Series Outliers”, Journal of Time Series Analysis, 9(2), 109-119, 1988.
• Bruce, A.G., Martin, D., “Leave-k-out diagnostics for time series (with discussion)”, Journal of the Royal Statistical Society Series B, 51, 363-424, 1989.
• Abraham. B., Chuang, A., “Outlier Detection and Time Series Modelling”, Technometrics, 31(2), 241-248, 1989.
• Box, G.E.P., Tiao, G.C., “Intervention Analysis with Applications to Economic and Environmental Problems”, Journal of American Statistical Association, 70(349), 70-79, 1975.
• Denby. L., Martin, R.D., “Robust estimation of the first order autoregressive parameter”. The Journal of the American Statistical Association, 74, 140-146, 1979.
• Maronna, R., Martin, R., Yohai, V., Robust Statistics: Theory and Methods, Wiley, New York, USA, 2006.
• Firmino, P.R.A., Mattos Neto, P.S.G., “Ferreira TAE. Correcting and combining time series forecasters”, Neural Networks, 50, 1-11, 2014.
• Paulino, J., Gomes, C., Gonçalves Júnior, J., Rodrigues, M., Souza, A., Pimentel, J., Brito, K., Saboia, S., Firmino, P., “Predictive Models and Health Sciences: A Brief Analysis”. International Archives of Medicine. 2017. Doi: 10.3823/2487.
• Chen, Z., Chen, Y., Li, T., “Port cargo through put forecasting based on combination model”, Proceedings of in Joint International Information Technology. Mechanical and Electronic Engineering Conference (JIMEC 2016). Xi'an, China, 2016,148-154.
• Adedia, D., Nanga, S., Appiah, S.K., Lotsi, A., Abaye, D.A., “Box-Jenkins’ Methodology in Predicting Maternal Mortality Records from a Public Health Facility in Ghana”, Open Journal of Applied Sciences, 8, 189-202, 2018. Doi:10.4236/ojapps.2018.86016
• Langat, A., Orwa, G., Koima, J., “Cancer Cases in Kenya; Forecasting Incidents Using Box &Jenkins Arima Model”, Biomedical Statistics and Informatics, 2, 37-48, 2017.
• Aboagye-Sarfo, P., Mai, Q., Sanfilippo, F.M., Preen, D.B., Stewart, L.M., Fatovich, D.M., “A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia”, Journal of Biomedical Informatics, 57, 62-73, 2015. Doi: 10.1016/j.jbi.2015.06.022
• Luz, P.M., Mendes, B.V.M., Codeço, C.T., Struchiner, C.J., Galvani, A. P., “Time Series Analysis of Dengue Incidence in Rio de Janeiro, Brazil”, The American Journal of Tropical Medicine and Hygiene, 79(6), 933-939, 2008. Doi:10.4269/ajtmh.2008.79.933
• Aydın, Ö., Karaarslan, E., “Covid-19 Belirtilerinin Tespiti İçin Dijital İkiz Tabanlı Bir Sağlık Bilgi Sistemi”. Online International Conference of COVID-19 (CONCOVID), İstanbul, Turkey,2020, pp.8-9.
• Usman, M., Wajid, M., Zubair, M., Ahmed, A., “On the possibility of using Speech to detect COVID-19 symptoms: An overview and proof of concept”, Researchgate, 2020. Doi:10.13140/RG.2.2.31718.57923
• Munsch, N., Martin, A., Gruarin, S., Nateqi, J., Abdarahmane, I., Weingartner-Ortner, R., Knapp, B., “A benchmark of online COVID-19 symptom checkers”, medRxiv 2020.05.22.20109777, 2020. Doi: 10.1101/2020.05.22.20109777
• Mackey, T. K., Purushothaman, V. L., Li, J., Shah, N., Nali, M., Bardier, C., Liang, B., Cai, M., Cuomo, R., “Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated with COVID-19 on Twitter: Retrospective Big Data Infoveillance Study”, JMIR Public Health and Surveillance, 6(2), e19509, 2020. Doi:10.2196/19509.
• Zens, M., Brammertz, A., Herpich, J., Suedkamp, N. P., Hinterseer, M., “App-based tracking of self-reported COVID-19 symptoms (Preprint)”. ResearchGate, 2020. Doi:10.2196/preprints.21956
• Louden, K.C., Laudon, J.P., Management Information Systems: managing the digital firm (8th. edition), Prentice-Hall, New Jersey, USA, 2004.
• Kini, R.B., “Strategic Information Systems”, Information Systems Management. 10(4), 42-50, 1993.
• Ammenwerth, E., Graber, S., Herrmann, G., Bürkle, T., König, J., “Evaluation of Health Information Systems-Problems and Challenges”. International Journal of Medical Informatics, 71, 125-135, 2003.
• Ljung, G.M., “On Outlier Detection in Time Series”. Journal of the Royal Statistical Society: Series B, 55, 559-567, 1993.
• Kaya, A., “A Type Outlier in AR (1) Model”, The Journal of Statisticians, 3, 1-7, 2010.
• Box, G.E.P., Jenkins, G.M., Time series analysis: Forecasting and control, Holden-Day, San Francisco, USA, 1976.
• Kaya, A., “Outlier Effects on Databases, LNCS 3261, Advancing Information Systems.” Proceedings of Third International Conference, ADVIS 2004, İzmir, Turkey, Springer, 2004. p. 88-96.
• Kılıç, T., “Digital Hospital; An Example of Best Practice”. International Journal of Health Services Research and Policy, 1(2), 52-58, 2016. Doi:10.23884/ijhsrp.2016.1.2.04