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Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks

Yıl 2018, Cilt: 11 Sayı: 4, 357 - 368, 30.10.2018
https://doi.org/10.17671/gazibtd.444826

Öz

Turnaround
time (TAT) or duration between different stages in medical and healthcare
services is accepted to be one of the most significant performance measures
that can have a great impact on service quality, change management, costs, and
strategic decisions. Accurate and reliable prediction or estimation of the
turnaround times or elicitation of the underlying causes that affect TAT is
known to be a difficult problem. In this study, a heuristic prediction approach
is used by designing and implementing a special artificial neural network (ANN)
model in order to predict TAT of a specific process in a private hospital. The
prediction performance of our ANN model is comparatively analyzed with some
alternative linear and nonlinear numerical prediction algorithms. The results
show that ANN surpasses all of the other numerical prediction algorithms and
ANN might be used by the decision makers as a reliable model to estimate TAT
within acceptable error rates.

Kaynakça

  • [1] R. Bernardi, P. Constantinides, J. Nandhakumar, “Challenging Dominant Frames in Policies for IS Innovation in Healthcare Through Rhetorical Strategies”, Journal of the Association for Information Systems, 18(2), 81-112, 2017.
  • [2] S. Dilek, S. Özdemir, “Wireless Sensor Networks in Healthcare”, International Journal of Informatics Technologies, 7(2), 7-19, 2014.
  • [3] C. He, X. Fan, Y. Li, “Toward Ubiquitous Healthcare Services with a Novel Efficient Cloud Platform”, IEEE Transactions on Biomedical Engineering, 60(1), 230-234, 2013.
  • [4] A. R. Lyon, J. K. Wasse, K. Ludwig, M. Zachry, E. J. Bruns, J. Unutzer, E., McCauley, “The Contextualized Technology Adaptation Process (CTAP): Optimizing Health Information Technology to Improve Mental Health Systems. Administration and Policy”, Mental Health and Mental Health Services Research, 43(3), 394-409, 2016.
  • [5] X. W. Ng, W. Y. Chung, “VLC-Based Medical Healthcare Information System”, Biomedical Engineering: Applications, Basis and Communications, 24(2), 155-163, 2012.
  • [6] H. M. Söderholm, D. H. Sonnenwald, “Visioning Future Emergency Healthcare Collaboration: Perspectives from Large and Small Medical Centers”, Journal of the American Society for Information Science and Technology, 61(9), 1808–1823, 2010.
  • [7] A. Demir, E. İ. Tatlı, “Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications”, International Journal of Informatics Technologies, 11(1), 1-8, 2018.
  • [8] B. Goswami, B. Singh, R. Chawla, V. K. Gupta, V. Mallika, “Turnaround Time (TAT) as a Benchmark of Laboratory Performance”, Indian Journal of Clinical Biochemistry, 25(4), 376-379, 2010.
  • [9] İ. H. Köksal, B. Türkoğlu, M. Eminağaoğlu, “An Adaptive Network-Based Fuzzy Inference System for Estimating the Duration of Medical Services: A Case Study”, IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, 801-806, 2016.
  • [10] M. Scagliarini, M. Apreda, U. Wienand, G. Valpiani, “Monitoring Operating Room Turnaround Time: A Retrospective Analysis”, International Journal of Health Care Quality Assurance, 29(3), 351-359, 2016.
  • [11] D. Sinreich, Y. Marmor, “Ways to Reduce Patient Turnaround Time and Improve Service Quality in Emergency Departments”, Journal of Health Organization and Management, 19(2), 88-105, 2005.
  • [12] K. A. Willoughby, B. T. B. Chan, M. Strenger, “Achieving Wait Time Reduction in the Emergency Department”, Leadership in Health Services, 23(4), 304-319, 2010.
  • [13] B. Breil, F. Fritz, V. Thiemann, M. Dugas, “Mapping Turnaround Times (TAT) to A Generic Timeline: A Systematic Review of TAT Definitions in Clinical Domains”, BMC Medical Informatics and Decision Making, 11(34), 1-12, 2011.
  • [14] M. Fieri, N. F. Ranney, E. B. Schroeder, E. M. Van Aken, A. H. Stone, “Analysis and Improvement of Patient Turnaround Time in an Emergency Department”, IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, USA, 239-244, 2010.
  • [15] A. B. Storrow, C. Zhou, G. Gaddis, J. H. Han, K. Miller, D. Klubert, A. Laidig, D. Aronsky, “Decreasing Lab Turnaround Time Improves Emergency Department Throughput and Decreases Emergency Medical Services Diversion: A Simulation Model”, Academic Emergency Medicine, 15(11), 1130-1135, 2008.
  • [16] C. Hand, H. Mannila, P. Smyth, Principles of Data Mining, MIT Press, London, 2001.
  • [17] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2nd ed., Springer, New York, 2009.
  • [18] N. Nedjah, M. Luiza, J. Kacprzyk, Innovative Applications in Data Mining, Springer-Verlag, Berlin, 2009.
  • [19] I. H. Witten, E. Frank, M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, 2011.
  • [20] E. Alpaydın, Introduction to Machine Learning, 2nd ed., MIT Press, 2010.
  • [21] S. Özden, A. Öztürk, “Electricity Energy Demand Forecasting for an Industrial Region (Ivedik) by using Artificial Neural Network and Time Series”, Bilişim Teknolojileri Dergisi, 11(3), 255-261, 2018.
  • [22] E. S. Olivas, J. D. M. Guerrero, M. M. Sober, J. R. M. Benedito, A. J. S. Lopez, Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global, 2009.
  • [23] J. Reyes, A. Morales-Esteban, F. Martinez-Alvarez, “Neural Networks to Predict Earthquakes in Chile”, Applied Soft Computing, 13(2), 1314-1328, 2013.
  • [24] E. Çelik, O. Çavuşoğlu, H. Gürün, N. Öztürk, “Estimation of the Clearance Effect in the Blanking Process of CuZn30 Sheet Metal Using Neural Network−A Comparative Study”, Bilişim Teknolojileri Dergisi, 11(2), 187-193, 2018.
  • [25] Ö. Tanidir, O.B. Tör, “Accuracy of ANN Based Day-Ahead Load Forecasting in Turkish Power System: Degrading and Improving Factors”, Neural Network World, 25(4), 443–456, 2015.
  • [26] S. Senan, “A Neural Net-Based Approach for CPU Utilization”, Bilişim Teknolojileri Dergisi, 10(3), 263-272, 2017.
  • [27] S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson Education, Inc., New Jersey, 2009.
  • [28] J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers, San Francisco, 2006.
  • [29] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks, Springer-Verlag, Berlin, 2012.
  • [30] T. Dasu, T. Johnson, Exploratory Data Mining and Data Cleaning, John Wiley & Sons Inc., New Jersey, 2003.
  • [31] H. Özkişi, M. Topaloğlu, “The Estimation of the Photovoltaic Cell Productivity with the Use of Artificial Neural Network”, Bilişim Teknolojileri Dergisi, 10(3), 247-253, 2017.
  • [32] C. D. Ravinesh, M. Şahin, “Application of the Extreme Learning Machine Algorithm for the Prediction of Monthly Effective Drought Index in Eastern Australia”, Applied Soft Computing, 15(3), 512-525, 2015.
  • [33] I-C. Yeh, C. Lien, “The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients”, Expert Systems with Applications, 36, 2473-2480, 2009.
  • [34] M. H., Calp, An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks, Journal of Polytechnic, DOI: 10.2339/politeknik.444380, 2019. (In Press).
  • [35] D. T. Larose, Data Mining Methods and Models, John Wiley & Sons Inc., New Jersey, 2006.
  • [36] D. T. Larose, Discovering Knowledge in Data - An Introduction to Data Mining, John Wiley & Sons Inc., New Jersey, 2005.
  • [37] Internet: Machine Learning Group at the University of Waikato, http://www.ss.waikato.ac.nz/ml/Weka/, 03.05.2018.
  • [38] D. W. Aha, D. Kibler, M. K. Albert, “Instance-Based Learning Algorithms”, Machine Learning, 6(1), 37-66, 1991.
  • [39] J. G. Cleary, L. E. Trigg, “K*: An Instance-Based Learner Using an Entropic Distance Measure”, 12th International Conference on Machine Learning, Tahoe City, California, USA, 108-114, 1995.
  • [40] H. Akaike, “Likelihood of a Model and Information Criteria”, Journal of Econometrics, 16(1), 3-14, 1981.
  • [41] P. J. Rousseeuw, A. M. Leroy, Robust Regression and Outlier Detection, John Wiley & Sons Inc., 1987.
  • [42] R. J. Quinlan, “Learning with Continuous Classes”, 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.
  • [43] Y. Wang, I. H., Witten, “Induction of Model Trees for Predicting Continuous Classes”, 9th European Conference on Machine Learning, Prague, Czech Republic, 1997.
  • [44] W. Iba, P. Langley, “Induction of One-Level Decision Trees”, 9th International Conference on Machine Learning, Aberdeen, Scotland, 233–240, 1992.
  • [45] M. D. Buhmann, Radial Basis Functions: Theory and Implementations, Cambridge University Press, UK, 2003.
  • [46] J. Platt, “Fast Training of Support Vector Machines Using Sequential Minimal Optimization”, Advances in Kernel Methods - Support Vector Learning, editors: B. Schoelkopf, C. Burges, A. Smola, MIT Press, 1998.
  • [47] S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, K.R.K. Murthy, “Improvements to the SMO Algorithm for SVM Regression”, IEEE Transactions on Neural Networks, 11(5), 1188-1193, 2000.

Yapay Sinir Ağları ile Tıbbi Laboratuvar için İşlem Süresi Kestirimi

Yıl 2018, Cilt: 11 Sayı: 4, 357 - 368, 30.10.2018
https://doi.org/10.17671/gazibtd.444826

Öz

Hastanelerde ve çeşitli sağlık hizmetlerinde tıbbi işlemler
/ aşamalar arasındaki işlem ya da geri dönüş süresi, hizmet kalitesi, değişim
yönetimi, maliyetlerin azaltılması ve stratejik kararlar üzerinde de etkisi olan
en önemli performans ölçütlerinden biri olarak kabul edilmektedir. Geri dönüş
sürelerinin doğru ve güvenilir tahmini ya da bu süreleri etkileyen etmenlerin
veya nedenlerin ortaya çıkarılması ise çözümü zor bir problemdir. Bu çalışmada,
özel bir hastanedeki çeşitli birimler arasındaki tıbbi iş süreçlerine ait
gerçek istatistiksel işlem süresi verileri kullanılarak iş bitirme sürelerinin
sayısal olarak tahmini için özel bir yapay sinir ağı (YSA) modeli tasarlanmış
ve kodlanmıştır. YSA modelimizin kestirim performansı, bazı alternatif doğrusal
/ doğrusal olmayan sayısal kestirim algoritmaları ile karşılaştırmalı olarak
analiz edilmiştir. YSA'nın tahmin başarısı ve hata değerleri açısından diğer
tüm sayısal kestirim algoritmalarından daha başarılı olduğu ve YSA'nın, tıbbi
iş süreçlerinde iş bitirme sürelerini kabul edilebilir hata oranlarında güvenilebilir
şekilde tahmin edebildiği ve karar destek sistemlerinde yöneticiler tarafından
alternatif bir model olarak kullanılabileceği ortaya konmuştur.

Kaynakça

  • [1] R. Bernardi, P. Constantinides, J. Nandhakumar, “Challenging Dominant Frames in Policies for IS Innovation in Healthcare Through Rhetorical Strategies”, Journal of the Association for Information Systems, 18(2), 81-112, 2017.
  • [2] S. Dilek, S. Özdemir, “Wireless Sensor Networks in Healthcare”, International Journal of Informatics Technologies, 7(2), 7-19, 2014.
  • [3] C. He, X. Fan, Y. Li, “Toward Ubiquitous Healthcare Services with a Novel Efficient Cloud Platform”, IEEE Transactions on Biomedical Engineering, 60(1), 230-234, 2013.
  • [4] A. R. Lyon, J. K. Wasse, K. Ludwig, M. Zachry, E. J. Bruns, J. Unutzer, E., McCauley, “The Contextualized Technology Adaptation Process (CTAP): Optimizing Health Information Technology to Improve Mental Health Systems. Administration and Policy”, Mental Health and Mental Health Services Research, 43(3), 394-409, 2016.
  • [5] X. W. Ng, W. Y. Chung, “VLC-Based Medical Healthcare Information System”, Biomedical Engineering: Applications, Basis and Communications, 24(2), 155-163, 2012.
  • [6] H. M. Söderholm, D. H. Sonnenwald, “Visioning Future Emergency Healthcare Collaboration: Perspectives from Large and Small Medical Centers”, Journal of the American Society for Information Science and Technology, 61(9), 1808–1823, 2010.
  • [7] A. Demir, E. İ. Tatlı, “Security Analysis of Medical Devices within Wireless Body Area Networks and Mobile Health Applications”, International Journal of Informatics Technologies, 11(1), 1-8, 2018.
  • [8] B. Goswami, B. Singh, R. Chawla, V. K. Gupta, V. Mallika, “Turnaround Time (TAT) as a Benchmark of Laboratory Performance”, Indian Journal of Clinical Biochemistry, 25(4), 376-379, 2010.
  • [9] İ. H. Köksal, B. Türkoğlu, M. Eminağaoğlu, “An Adaptive Network-Based Fuzzy Inference System for Estimating the Duration of Medical Services: A Case Study”, IEEE 10th International Conference on Application of Information and Communication Technologies (AICT), Baku, Azerbaijan, 801-806, 2016.
  • [10] M. Scagliarini, M. Apreda, U. Wienand, G. Valpiani, “Monitoring Operating Room Turnaround Time: A Retrospective Analysis”, International Journal of Health Care Quality Assurance, 29(3), 351-359, 2016.
  • [11] D. Sinreich, Y. Marmor, “Ways to Reduce Patient Turnaround Time and Improve Service Quality in Emergency Departments”, Journal of Health Organization and Management, 19(2), 88-105, 2005.
  • [12] K. A. Willoughby, B. T. B. Chan, M. Strenger, “Achieving Wait Time Reduction in the Emergency Department”, Leadership in Health Services, 23(4), 304-319, 2010.
  • [13] B. Breil, F. Fritz, V. Thiemann, M. Dugas, “Mapping Turnaround Times (TAT) to A Generic Timeline: A Systematic Review of TAT Definitions in Clinical Domains”, BMC Medical Informatics and Decision Making, 11(34), 1-12, 2011.
  • [14] M. Fieri, N. F. Ranney, E. B. Schroeder, E. M. Van Aken, A. H. Stone, “Analysis and Improvement of Patient Turnaround Time in an Emergency Department”, IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, USA, 239-244, 2010.
  • [15] A. B. Storrow, C. Zhou, G. Gaddis, J. H. Han, K. Miller, D. Klubert, A. Laidig, D. Aronsky, “Decreasing Lab Turnaround Time Improves Emergency Department Throughput and Decreases Emergency Medical Services Diversion: A Simulation Model”, Academic Emergency Medicine, 15(11), 1130-1135, 2008.
  • [16] C. Hand, H. Mannila, P. Smyth, Principles of Data Mining, MIT Press, London, 2001.
  • [17] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2nd ed., Springer, New York, 2009.
  • [18] N. Nedjah, M. Luiza, J. Kacprzyk, Innovative Applications in Data Mining, Springer-Verlag, Berlin, 2009.
  • [19] I. H. Witten, E. Frank, M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, 2011.
  • [20] E. Alpaydın, Introduction to Machine Learning, 2nd ed., MIT Press, 2010.
  • [21] S. Özden, A. Öztürk, “Electricity Energy Demand Forecasting for an Industrial Region (Ivedik) by using Artificial Neural Network and Time Series”, Bilişim Teknolojileri Dergisi, 11(3), 255-261, 2018.
  • [22] E. S. Olivas, J. D. M. Guerrero, M. M. Sober, J. R. M. Benedito, A. J. S. Lopez, Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global, 2009.
  • [23] J. Reyes, A. Morales-Esteban, F. Martinez-Alvarez, “Neural Networks to Predict Earthquakes in Chile”, Applied Soft Computing, 13(2), 1314-1328, 2013.
  • [24] E. Çelik, O. Çavuşoğlu, H. Gürün, N. Öztürk, “Estimation of the Clearance Effect in the Blanking Process of CuZn30 Sheet Metal Using Neural Network−A Comparative Study”, Bilişim Teknolojileri Dergisi, 11(2), 187-193, 2018.
  • [25] Ö. Tanidir, O.B. Tör, “Accuracy of ANN Based Day-Ahead Load Forecasting in Turkish Power System: Degrading and Improving Factors”, Neural Network World, 25(4), 443–456, 2015.
  • [26] S. Senan, “A Neural Net-Based Approach for CPU Utilization”, Bilişim Teknolojileri Dergisi, 10(3), 263-272, 2017.
  • [27] S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson Education, Inc., New Jersey, 2009.
  • [28] J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd ed., Morgan Kaufmann Publishers, San Francisco, 2006.
  • [29] A. Graves, Supervised Sequence Labelling with Recurrent Neural Networks, Springer-Verlag, Berlin, 2012.
  • [30] T. Dasu, T. Johnson, Exploratory Data Mining and Data Cleaning, John Wiley & Sons Inc., New Jersey, 2003.
  • [31] H. Özkişi, M. Topaloğlu, “The Estimation of the Photovoltaic Cell Productivity with the Use of Artificial Neural Network”, Bilişim Teknolojileri Dergisi, 10(3), 247-253, 2017.
  • [32] C. D. Ravinesh, M. Şahin, “Application of the Extreme Learning Machine Algorithm for the Prediction of Monthly Effective Drought Index in Eastern Australia”, Applied Soft Computing, 15(3), 512-525, 2015.
  • [33] I-C. Yeh, C. Lien, “The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients”, Expert Systems with Applications, 36, 2473-2480, 2009.
  • [34] M. H., Calp, An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks, Journal of Polytechnic, DOI: 10.2339/politeknik.444380, 2019. (In Press).
  • [35] D. T. Larose, Data Mining Methods and Models, John Wiley & Sons Inc., New Jersey, 2006.
  • [36] D. T. Larose, Discovering Knowledge in Data - An Introduction to Data Mining, John Wiley & Sons Inc., New Jersey, 2005.
  • [37] Internet: Machine Learning Group at the University of Waikato, http://www.ss.waikato.ac.nz/ml/Weka/, 03.05.2018.
  • [38] D. W. Aha, D. Kibler, M. K. Albert, “Instance-Based Learning Algorithms”, Machine Learning, 6(1), 37-66, 1991.
  • [39] J. G. Cleary, L. E. Trigg, “K*: An Instance-Based Learner Using an Entropic Distance Measure”, 12th International Conference on Machine Learning, Tahoe City, California, USA, 108-114, 1995.
  • [40] H. Akaike, “Likelihood of a Model and Information Criteria”, Journal of Econometrics, 16(1), 3-14, 1981.
  • [41] P. J. Rousseeuw, A. M. Leroy, Robust Regression and Outlier Detection, John Wiley & Sons Inc., 1987.
  • [42] R. J. Quinlan, “Learning with Continuous Classes”, 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.
  • [43] Y. Wang, I. H., Witten, “Induction of Model Trees for Predicting Continuous Classes”, 9th European Conference on Machine Learning, Prague, Czech Republic, 1997.
  • [44] W. Iba, P. Langley, “Induction of One-Level Decision Trees”, 9th International Conference on Machine Learning, Aberdeen, Scotland, 233–240, 1992.
  • [45] M. D. Buhmann, Radial Basis Functions: Theory and Implementations, Cambridge University Press, UK, 2003.
  • [46] J. Platt, “Fast Training of Support Vector Machines Using Sequential Minimal Optimization”, Advances in Kernel Methods - Support Vector Learning, editors: B. Schoelkopf, C. Burges, A. Smola, MIT Press, 1998.
  • [47] S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, K.R.K. Murthy, “Improvements to the SMO Algorithm for SVM Regression”, IEEE Transactions on Neural Networks, 11(5), 1188-1193, 2000.
Toplam 47 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Mete Eminağaoğlu 0000-0003-2456-919X

Alper Vahaplar

Yayımlanma Tarihi 30 Ekim 2018
Gönderilme Tarihi 17 Temmuz 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 11 Sayı: 4

Kaynak Göster

APA Eminağaoğlu, M., & Vahaplar, A. (2018). Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks. Bilişim Teknolojileri Dergisi, 11(4), 357-368. https://doi.org/10.17671/gazibtd.444826