Time Series Cleaning Methods for Hospital Emergency Admissions

Year 2022, Volume: 3 Issue: 1, 34 - 40, 28.06.2022

Yiğit Alişan Olcay Tosun

https://doi.org/10.55195/jscai.1126611

Abstract

Due to the nature of hospital emergency services, density cannot be easily estimated. It is one of the important issues that should be planned for emergency service managers to have sufficient resources continuously in services that develop suddenly, and emergency interventions are made for human life. Effective and efficient management and planning of limited resources are important not only for hospital administrators but also for people who will receive service from emergency services. In this situation, estimating the number of people who will request service in the emergency service with the least error is of great importance in terms of resource management and the operations carried out in the emergency services. The density of patients coming to the emergency department may vary according to the season, special dates, and even time zones during the day. The aim of the study is to show that more successful results will be obtained because of processing the time series by considering the country and area-specific features instead of the traditional approach. In this paper, the patient admission dataset of the public hospital emergency service in Turkey was used. Data cleaning and arranging operations were carried out by considering the official and religious special days of Turkey and the time periods during the day. The data set is first handled holistically, and its performances are measured by making predictions with the LSTM (Long Short Term Memory) model. Then, to examine the effect of time zones, performance values were calculated separately by dividing each day into 3 equal time zones. Finally, to investigate the effect of triage areas on the total density, the model performance was measured by dividing the data forming each time zone into 3 different triage areas in 3 equal time periods. Three stages were applied both on the raw data set and on the data created by extracting the official, religious holidays, and weekend data specific to Turkey. According to the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) results, more successful results are obtained thanks to the cleaning and editing processes. Thanks to the study, it is thought that the data sets used for demand forecasting studies in the health sector will produce results closer to reality by determining and standardizing the purification criteria in this way.

Keywords

LSTM, Data Cleaning, Emergency services, Time Series

References

• “Başbakanlık Mevzuatı Geliştirme ve Yayın Genel Müdürlüğü.” https://www.resmigazete.gov.tr/eskiler/2009/10/20091016-16.htm (accessed May 14, 2021).
• T. R. M. Azeredo, H. M. Guedes, R. A. Rebelo de Almeida, T. C. M. Chianca, and J. C. A. Martins, “Efficacy of the Manchester Triage System: a systematic review,” International Emergency Nursing, vol. 23, no. 2, pp. 47–52, Apr. 2015, doi: 10.1016/j.ienj.2014.06.001.
• Ö. Serper and N. Gürsakal, Araştırma Yöntemleri Üzerine, Filiz Kitabevi. İstanbul, 1983.
• K. Gürtan, İstatistik ve araştırma metotları. İstanbul Üniversitesi, 1971.
• F. Serin, Y. Alisan, and A. Kece, “Hybrid time series forecasting methods for travel time prediction,” Physica A: Statistical Mechanics and its Applications, vol. 579, p. 126134, Oct. 2021, doi: 10.1016/j.physa.2021.126134.
• F. Serin, Y. Alisan, and M. Erturkler, “Predicting Bus Travel Time Using Machine Learning Methods with Three-Layer Architecture,” Measurement, p. 111403, May 2022, doi: 10.1016/j.measurement.2022.111403.
• E. Özkan, E. Güler, and Z. Aladağ, “Elektrik Enerjisi Tüketim Verileri İçin Uygun Tahmin Yöntemi Seçimi,” Endüstri Mühendisliği, vol. 31, no. 2, pp. 198–214, 2020.
• G. Altınay, “Aylık elektrik talebinin mevsimsel model ile orta dönem öngörüsü,” 2010.
• O. Çoban and C. C. Özcan, “Sektörel Açidan Enerjinin Artan Önemi: Konya İli İçin Bir Doğalgaz Talep Tahmini Denemesi,” Sosyal Ekonomik Araştırmalar Dergisi, vol. 11, no. 22, pp. 85–106, 2011.
• Yucesan, M., Pekel, E., Celik, E., Gul, M., & Serin, F. (2021). Forecasting daily natural gas consumption with regression, time series and machine learning based methods. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1-16.
• O. Kaynar, S. Taştan, and F. Demirkoparan, “Yapay sinir ağlari ile doğalgaz tüketim tahmini,” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 25, 2011.
• M. Akdağ and V. Yiğit, “Box-Jenkins Ve Yapay Sinir Aği Modelleri İle Enflasyon Tahmini,” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 30, no. 2, 2016.
• A. Bekin, “Türkiye’de bazı temel gıda fiyatları için yapay sinir ağları ve zaman serisi tahmin modellerinin karşılaştırmalı analizi,” Master’s Thesis, 2015.
• B. Ataseven, “Yapay sinir ağlari ile öngörü modellemesi,” Öneri Dergisi, vol. 10, no. 39, pp. 101–115, 2013.
• M. Yucesan, M. Gul, and E. Celik, “Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry,” Drvna industrija: Znanstveni časopis za pitanja drvne tehnologije, vol. 69, no. 4, pp. 357–370, 2018.
• G. Zhang, X. Zhang, and H. Feng, “Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion,” Expert Systems, vol. 33, no. 5, pp. 501–516, 2016.
• C. Yuan, S. Liu, and Z. Fang, “Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model,” Energy, vol. 100, pp. 384–390, 2016.
• R. Tripathi et al., “Forecasting rice productivity and production of Odisha, India, using autoregressive integrated moving average models,” Advances in Agriculture, vol. 2014, 2014.
• G. Sariyer, “Acil Servislerde Talebin Zaman Serileri Modelleri ile Tahmin Edilmesi,” International Journal of Engineering Research and Development, vol. 10, no. 1, Art. no. 1, Jan. 2017, doi: 10.29137/umagd.419661.
• F. Serin , A. Keçe and Y. Alişan , "Applying Machine Learning Prediction Methods to COVID-19 Data", Journal of Soft Computing and Artificial Intelligence, vol. 3, no. 1, pp. 11-21, doi:10.55195/jscai.1108528
• V. Demir, M. Zontul, and İ. Yelmen, “Drug Sales Prediction with ACF and PACF Supported ARIMA Method,” in 2020 5th International Conference on Computer Science and Engineering (UBMK), Sep. 2020, pp. 243–247. doi: 10.1109/UBMK50275.2020.9219448.
• E. Aydemir, M. Karaatli, G. Yilmaz, and S. Aksoy, “112 Acil çağrı merkezine gelen çağrı sayılarını belirleyebilmek için bir yapay sinir ağları tahminleme modeli geliştirilmesi,” Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 20, no. 5, pp. 145–149, 2014.
• A. Sevda, “Türkiye’de Reçete Başına Ortalama Maliyet Serisinin Zaman Serisi Modelleriyle Öngörüsü ve Öngörü Performanslarının Karşılaştırılması,” SGD-Sosyal Güvenlik Dergisi, vol. 4, no. 2, pp. 176–192.
• A. G. Özüdoğru and A. Görener, “Sağlık sektöründe talep tahmini üzerine bir uygulama,” 2015.
• Kaya, Y., Kuncan, F. & Tekin, R. A New Approach for Congestive Heart Failure and Arrhythmia Classification Using Angle Transformation with LSTM. Arab J Sci Eng (2022). https://doi.org/10.1007/s13369-022-06617-8
• V. Yiğit, “Hastanelerde tibbi malzeme talep tahmini: Serum seti tüketimi üzerinde örnek bir uygulama,” Manas Sosyal Araştırmalar Dergisi, vol. 5, no. 4, pp. 207–222, 2016.
• Ortiz-Barrios, M., Gul, M., Yucesan, M., Alfaro-Sarmiento, I., Navarro-Jiménez, E., & Jiménez-Delgado, G. (2022). A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case. International journal of disaster risk reduction, 72, 102831.
• A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105. Accessed: Apr. 27, 2020. [Online]. Available: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
• I. J. Goodfellow et al., “Generative Adversarial Networks,” arXiv:1406.2661 [cs, stat], Jun. 2014, Accessed: May 13, 2021. [Online]. Available: http://arxiv.org/abs/1406.2661
• C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” arXiv:1512.00567 [cs], Dec. 2015, Accessed: May 13, 2021. [Online]. Available: http://arxiv.org/abs/1512.00567
• J. Cao, Z. Li, and J. Li, “Financial time series forecasting model based on CEEMDAN and LSTM,” Physica A: Statistical Mechanics and its Applications, vol. 519, pp. 127–139, Apr. 2019, doi: 10.1016/j.physa.2018.11.061.
• A. Sagheer and M. Kotb, “Time series forecasting of petroleum production using deep LSTM recurrent networks,” Neurocomputing, vol. 323, pp. 203–213, Jan. 2019, doi: 10.1016/j.neucom.2018.09.082.
• S. Kaushik et al., “AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures,” Front. Big Data, vol. 3, 2020, doi: 10.3389/fdata.2020.00004.
• V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks,” Chaos, Solitons & Fractals, vol. 135, p. 109864, Jun. 2020, doi: 10.1016/j.chaos.2020.109864.
• F. Kadri, F. Harrou, S. Chaabane, and C. Tahon, “Time series modelling and forecasting of emergency department overcrowding,” Journal of medical systems, vol. 38, no. 9, p. 107, 2014.
• S. Khadanga, K. Aggarwal, S. Joty, and J. Srivastava, “Using Clinical Notes with Time Series Data for ICU Management,” arXiv:1909.09702 [cs, stat], Jan. 2020, Accessed: May 13, 2021. [Online]. Available: http://arxiv.org/abs/1909.09702
• Z. C. Lipton, D. C. Kale, and R. C. Wetzel, “Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks,” arXiv:1510.07641 [cs], Mar. 2017, Accessed: May 13, 2021. [Online]. Available: http://arxiv.org/abs/1510.07641
• A.Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke and J. Schmidhuber “A Novel Connectionist System for Unconstrained Handwriting Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, pp. 855-868, May 2009, doi: 10.1109/TPAMI.2008.137
• V. Märgner and H. E. Abed, “ICDAR 2009 Arabic Handwriting Recognition Competition,” in 2009 10th International Conference on Document Analysis and Recognition, Jul. 2009, pp. 1383–1387. doi: 10.1109/ICDAR.2009.256.
• C. Metz, “An Infusion of AI Makes Google Translate More Powerful Than Ever,” Wired. Accessed: May 13, 2021. [Online]. Available: https://www.wired.com/2016/09/google-claims-ai-breakthrough-machine-translation/
• S. Siami-Namini and A. S. Namin, “Forecasting Economics and Financial Time Series: ARIMA vs. LSTM,” arXiv:1803.06386 [cs, q-fin, stat], Mar. 2018, Accessed: May 13, 2021. [Online]. Available: http://arxiv.org/abs/1803.06386
• S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
• K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber, “LSTM: A Search Space Odyssey,” IEEE Trans. Neural Netw. Learning Syst., vol. 28, no. 10, pp. 2222–2232, Oct. 2017, doi: 10.1109/TNNLS.2016.2582924.
• F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: continual prediction with LSTM,” pp. 850–855, Jan. 1999, doi: 10.1049/cp:19991218.
• F. A. Gers, D. Eck, and J. Schmidhuber, “Applying LSTM to time series predictable through time-window approaches,” in Neural Nets WIRN Vietri-01, Springer, 2002, pp. 193–200.
• N. Ahmadi, T. G. Constandinou, and C.-S. Bouganis, “Decoding hand kinematics from local field potentials using long short-term memory (LSTM) network,” in 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), 2019, pp. 415–419.
• Ç. Elmas, “Yapay sinir ağları,” Seçkin Yayıncılık :2003.
• E. Ulucan and İ. KIZILIRMAK, “Konaklama işletmelerinde talep tahmin yöntemleri: Yapay sinir ağları ile ilgili bir araştırma,” Seyahat ve Otel İşletmeciliği Dergisi, vol. 15, no. 1, pp. 89–101, 2018.
• S. Şahin And D. Kocadağ, “Sağlik Sektöründe Talep Tahmini Üzerine Literatür Araştirmasi,” Düzce Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 10, no. 1, pp. 99–113.
• C. D. Lewis, Industrial and business forecasting methods: a practical guide to exponential smoothing and curve fitting. London ; Boston: Butterworth Scientific, 1982.
• H. Kutbay, “Kayıt dışı istihdamın vergi gelirleri üzerindeki etkisi,” Sosyal Bilimler Araştırma Dergisi, vol. 7, no. 2, pp. 172–189, 2018.
• F. Y. Contuk and B. Güngör, “Finansal Piyasaların Gelişmesinin Ekonomik Büyüme Üzerine Etkileri: 1998-2014 Türkiye Örneği,” Finans Politik ve Ekonomik Yorumlar, no. 611, pp. 15–27, 2016.
• U. SİVRİ and B. SEVEN, “Ortalama Tüketim Eğilimi Durağan Mıdır? Türkiye Ekonomisi için Bir Zaman Serisi Analizi,” Anadolu İktisat ve İşletme Dergisi, vol. 1, no. 1, pp. 50–65, 2017.