Proceedings of International Conference on Applied Innovation in IT
2025/06/27, Volume 13, Issue 2, pp.269-278

Predicting Hospital Medicine Needs Based on a Multiple Linear Regression Model


Bashar Talib AL Nuaimi, Wedyan Habeeb Hameed and Wisam Sami Mohsin


Abstract: The study's issue is that hospitals are unable to accurately match the predicted demand and actual consumption for medications due to the use of traditional and ineffective forecasting techniques. The study's objective: 1) Finding a systematic approach based on modern scientific forecasting methods based on multi-factor mathematical models. 2) Selecting the optimal forecasting model among the methods utilized in this study to predict the future needs of hospitals for medicines in the coming years. 3) Calculating the discrepancy rate for each drug annually to check the forecasting accuracy. Three statistical analyses were performed: correlation and regression, time series, and retrospective forecast between estimated demand and actual consumption. Three accuracy indicators (MAPE, MAD, and MSD) were used as criteria for selecting the optimal model that best describes the pattern of future demands for six pharmaceuticals and five factors influencing drugs' consumption from 2015 to 2019. Three time series forecasting techniques (ALT, SES, and DES) were tested and compared with the MLR model to verify its forecasting accuracy. Time series techniques were compared to each other; DES was selected as the optimal technique among them. The results showed that the mean MAPE for all medications by using MLR and DES models was 15.63 and 24.61, respectively. Therefore, we conclude that MLR is the optimal model for hospital inventory management and forecasting future needs since it has a lower relative error rate compared to DES. This indicates that the influence of independent factors on demand is stronger than the time factor. Therefore, MLR outperformed DES, which relies on the time factor. With the exception of Infliximab 100mg and Tocilizumab 20mg/ml, whose values exceeded 25%, the average discrepancy rates between the estimated demand and actual consumption of each medication over a 5-year period are statistically significant and within the acceptable bounds.

Keywords: Correlation and Regression Analysis, Forecast, Future Needs, Multifactorial Model, Retrospective Analysis, Time Series Analysis.

DOI: 10.25673/120446

Download: PDF

References:

  1. W. H. Hameed, "Forecasting drug needs and quantification tools in healthcare institutions," Quantum Center, pp. 3-12, 2021.
  2. H. Abbasimehr, M. Shabani, and M. Yousefi, "An optimized model using LSTM network for demand forecasting," Computers & Industrial Engineering, vol. 143, p. 106435, 2020.
  3. E. Belyi, P. J. Giabbanelli, I. Patel, et al., "Combining association rule mining and network analysis for pharmacosurveillance," The Journal of Supercomputing, vol. 72, no. 5, pp. 2014-2024, 2016, [Online]. Available: https://doi.org/10.1007/s11227-016-1714-y.
  4. W. S. M. Al-Lami, "An economic study of some factors affecting the fiscal deficit in the Iraqi budget (2003-2016)," in Economics of Enterprises, Regions, and Countries: Current Issues and Modern Aspects, Penza: Library.Ru, 2021, pp. 19-22.
  5. H. Mohammed, I. Nazeeh, W. Alisawi, Q. Kadhim, and S. Ahmed, "Anomaly detection in human disease: a hybrid approach using GWO-SVM for gene selection," Revue d'Intelligence Artificielle, vol. 37, no. 4, pp. 913-919, 2023, [Online]. Available: https://doi.org/10.18280/ria.370411.
  6. H. A. A. Mohammed, S. T. Ahmed, R. M. H. Zaki, and Q. K. Kadhim, "Detection and localization of wrist fractures in x-ray imagery using deep learning teaching," Review of Computer Engineering Research, vol. 11, no. 3, pp. 85-98, 2024.
  7. A. Z. Kamaeva, "Multivariate forecasting of the need for anti-asthmatic drugs used in hospital settings (on the example of the Republic of Tatarstan)," 2013, p. 340.
  8. M. R. Safiullin, "Methodological approach to multivariate forecasting of hospital need for antibacterial drugs: on the example of destructive pancreatitis," Moscow: Russian State Library (RGB), 2018.
  9. O. V. Zhukova, "Methodology of assessment and forecasting of drug consumption based on clinical and economic analysis taking into account the principles of evidence-based medicine," 2021.
  10. F. Mbonyinshuti et al., "Application of random forest model to predict the demand of essential medicines for non-communicable diseases management in public health facilities," Pan African Medical Journal, vol. 42, p. 89, 2022, [Online]. Available: https://doi.org/10.11604/pamj.2022.42.89.33833.
  11. M. Rashid, V. Goyal, S. A. Parah, and H. Singh, "Drug prediction in healthcare using big data and machine learning," 2022.
  12. A. H. Nabizadeh, M. Ghaemi, and D. Gonçalves, "Predicting the pharmaceutical needs of hospitals," 2023, [Online]. Available: https://doi.org/10.21203/rs.3.rs-3263123/v1.
  13. D. M. Machado, Z. Yahouni, G. Alpan, and D. Koala, "Clustering analysis for forecasting medicine consumption," in Innovative Intelligent Industrial Production and Logistics, IN4PL 2024, Communications in Computer and Information Science, vol. 2372, Springer, Cham, 2025, pp. 10, [Online]. Available: https://doi.org/10.1007/978-3-031-80760-2_10.
  14. D. Koala, Z. Yahouni, G. Alpan, and D. S. Mohand, "Correlation analysis of factors impacting health product consumption in French hospitals," IFAC-PapersOnLine, vol. 55, no. 10, pp. 895-900, 2022, [Online]. Available: https://doi.org/10.1016/j.ifacol.2022.09.415.
  15. T. Falatouri, F. Darbanian, P. Brandtner, and C. Udokwu, "Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM," Procedia Computer Science, vol. 200, pp. 993-1003, 2022.
  16. H. W. Hameed, "Comparing linear trend analysis and double exponential smoothing techniques for predicting the demand for chronic medications," Automation in Industry, vol. 11, 2023, [Online]. Available: https://doi.org/10.25728/avtprom.2023.11.04.
  17. W. H. Hameed and E. M. Jassim, "Efficient time-series clustering of drugs demand to inventory optimization in health centers," Scientific and Educational Journal for Students and Teachers ‘StudNet’, no. 6, pp. 6905-6916, 2022.
  18. D. Bertsimas and N. Kallus, "From predictive to prescriptive analytics," Management Science, vol. 66, no. 3, pp. 1025-1044, 2020.
  19. P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, 3rd ed., New York: Springer, 2016, pp. 449.
  20. M. Zellner, A. E. Abbas, D. V. Budescu, and A. Galstyan, "A survey of human judgement and quantitative forecasting methods," Royal Society Open Science, vol. 8, no. 2, p. 201187, 2021.
  21. D. Koala, Z. Yahouni, G. Alpan, and Y. Frein, "Factors influencing drug consumption and prediction methods," in CIGI-QUALITA: Conférence Internationale Génie Industriel QUALITA, Grenoble, France, May 5-7, 2021, [Online]. Available: https://doi.org/10.48550/arXiv.2109.11779.
  22. F. Benhamida, O. Kaddouri, T. Ouhrouche, M. Benaichouche, D. Casado-Mansilla, and D. Lopez-de-Ipina, "Demand forecasting tool for inventory control smart systems," Journal of Communications Software and Systems, vol. 17, no. 2, pp. 185-196, June 2021, [Online]. Available: https://doi.org/10.24138/jcomss-2021-0068.
  23. A. I. Taloba, R. M. Abd El-Aziz, H. M. Alshanbari, and A. H. El-Bagoury, "Estimation and prediction of hospitalization and medical care costs using regression in machine learning," Journal of Healthcare Engineering, vol. 2022, p. 7969220, 2022, [Online]. Available: https://doi.org/10.1155/2022/7969220.
  24. L. V. Kobzar and T. A. Safronova, "The study of hormonal drug consumption by the method of expert assessments," Pharmacy, no. 4, pp. 5-9, 1981.

    HOME

       - Conference
       - Journal
       - Paper Submission to Journal
       - For Authors
       - For Reviewers
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 13, Issue 2 (ICAIIT 2025)
       - Volume 13, Issue 1 (ICAIIT 2025)
       - Volume 12, Issue 2 (ICAIIT 2024)
       - Volume 12, Issue 1 (ICAIIT 2024)
       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2025
         - Photos
         - Reports

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.