Deep learning for the prediction of trans-border logistics of patients to medical centers

• Autorzy: Arunrat Sawettham , Sumalee Ngeovwijit , Rapeepan Pitakaso , Chitpinan Charoenrungrueang , Supattraporn Saisomboon , Kosacka-Olejnik Monika

Identyfikatory

DOI

10.17270/J.LOG.2022.689

• Języki publikacji: EN

Abstrakty

EN

Background: Covid 19 impacted many healthcare logistics systems. An enormous number of people suffer from the effect of a pandemic, infection diseases can spread rapidly within and between countries. People from the Kingdom of Cambodia and the Lao People's Democratic Republic are most likely to cross-border into Thailand for diagnosis and special treatment. In this situation, international referral cannot predict the volume of patients and their destination. Therefore, the aim of the research is to use deep learning to construct a model that predicts the travel demand of patients at the border. Methods: Based on previous emergency medical services, the prediction demand used the gravity model or the regression model. The novelty element in this research paper uses the neural network technique. In this study, a two-stage survey is used to collect data. The first phase interviews experts from the strategic group level of The Public Health Office. The second phase examines the patient’s behavior regarding route selection using a survey. The methodology uses deep learning training using the Sigmoid function and Identity function. The statistics of precision include the average percent relative error (APRE), the root mean square error (RMSE), the standard deviation (SD), and the correlation coefficient (R). Results: Deep learning is suitable for complex problems as a network. The model allows the different data sets to forecast the demand for the cross-border patient for each hospital. Equations are applied to forecast demand, in which the different hospitals require a total of 58,000 patients per year to be diagnosed by the different hospitals. The predictor performs better than the RBF and regression model. Conclusions: The novelty element of this research uses the deep learning technique as an efficient nonlinear model; moreover, it is suitable for dynamic prediction. The main advantage is to apply this model to predict the number of patients, which is the key to determining the supply chain of treatment; additionally, the ability to formulate guidelines with healthcare logistics effectively in the future.

Słowa kluczowe

EN

international referral; healthcare logistic; deep learning; logistic model; COVID-19

PL

skierowanie międzynarodowe; logistyka opieki zdrowotnej; uczenie głębokie; model logistyczny; COVID-19

• Wydawca: Wyższa Szkoła Logistyki
• Czasopismo: LogForum
• Rocznik: 2022
• Tom: Vol. 18, no. 2
• Strony: 247--259
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Arunrat Sawettham

• International Business Management, Ubon Ratchathani Business School, Ubon Ratchathani University, Ubon Ratchathani, Thailand

autor

Sumalee Ngeovwijit

• International Business Management, Ubon Ratchathani Business School, Ubon Ratchathani University, Ubon Ratchathani, Thailand

autor

Rapeepan Pitakaso

• Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani, Thailand

• https://orcid.org/0000-0002-5896-4895

autor

Chitpinan Charoenrungrueang

• Business Management, Ubon Ratchathani Business School, Ubon Ratchathani University, Ubon Ratchathani, Thailand

autor

Supattraporn Saisomboon

• Business Management, Ubon Ratchathani Business School, Ubon Ratchathani University, Ubon Ratchathani, Thailand

autor

Kosacka-Olejnik Monika

• Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

• https://orcid.org/0000-0001-6950-2728

Bibliografia

• 1. Alam, S. T., Ahmed, S., Ali, S. M., Sarker, S., & Kabir, G., 2021, Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals. International Journal of Production Economics, 239, 108193. https://doi.org/10.1016/j.ijpe.2021.108193
• 2. Arcos-García, Á., Álvarez-García, J. A., & Soria-Morillo, L. M., 2018, Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods. Neural Networks, 99, 158-165, https://doi.org/10.1016/j.neunet.2018.01.005
• 3. Babatunde, S., Oloruntoba, R., & Agho, K., 2020, Healthcare commodities for emergencies in Africa: review of logistics models, suggested model and research agenda. Journal of Humanitarian Logistics and Supply Chain Management. https://doi.org/10.1108/JHLSCM-09-2019-0064
• 4. Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K., 2015, Big Data Analytics in Healthcare. BioMed Research International, 370194, https://doi.org/10.1155/2015/370194
• 5. Celikoglu, H. B., 2011, Travel time measure specification by functional approximation: application of radial basis function neural networks. Procedia - Social and Behavioral Sciences, 20(0), 613-620, https://doi.org/10.1016/j.sbspro.2011.08.068
• 6. Dossou, P.-E., Foreste, L., & Misumi, E., 2021, Intelligent Support System for Healthcare Logistics 4.0 Optimization in the Covid Pandemic Context. Journal of Software Engineering and Applications, 14(6), 233-256. https://doi.org/10.4236/jsea.2021.146014
• 7. Galetsi, P., & Katsaliaki, K., 2020, A review of the literature on big data analytics in healthcare. Journal of the Operational Research Society, 71(10), 1511-1529, https://doi.org/10.1080/01605682.2019.1630328
• 8. Gawrońska, A., & Nowak, F., 2017, Modelling medicinal products inventory management process in hospitals using a methodology based on the BPMN standard. Logforum, 13(4), 6. https://doi.org/10.17270/J.LOG.2017.4.6
• 9. Ghaderzadeh, M., & Asadi, F., 2021, Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review, Journal of Healthcare Engineering, 2021, 6677314, https://doi.org/10.1155/2021/6677314
• 10. Granillo-Macías, R., 2020, Inventory management and logistics optimization: a data mining practical approach, LogForum, 16(4)
• 11. Ha, Q. P., Wahid, H., Duc, H., & Azzi, M., 2015, Enhanced radial basis function neural networks for ozone level estimation, Neurocomputing, 155(0), 62-70, https://doi.org/10.1016/j.neucom.2014.12.048
• 12. Ilati, M., & Dehghan, M., 2015, The use of radial basis functions (RBFs) collocation and RBF-QR methods for solving the coupled nonlinear sine-Gordon equations. Engineering Analysis with Boundary Elements, 52(0), 99-109, https://doi.org/10.1016/j.enganabound.2014.11.023
• 13. Inanç, Ş., & Şenaras, A. E., 2020, An application for routing ambulance via ACO in home healthcare, In: transportation, logistics, and supply chain management in home healthcare: emerging research and opportunities, IGI Global, 102-110.
• 14. Kergosien, Y., Lenté, C., Billaut, J.-C., & Perrin, S., 2013, Metaheuristic algorithms for solving two interconnected vehicle routing problems in a hospital complex. Computers & Operations Research, 40(10), 2508-2518. https://doi.org/10.1016/j.cor.2013.01.009
• 15. Khanra, S., Dhir, A., Islam, A. K. M. N., & Mäntymäki, M., 2020, Big data analytics in healthcare: a systematic literature review. Enterprise Information Systems, 14(7), 878-912. https://doi.org/10.1080/17517575.2020.1812005
• 16. Kritchanchai, D., Krichanchai, S., Hoeur, S., & Tan, A., 2019, Healthcare supply chain management: macro and micro perspectives. LogForum, 15(4). https://doi.org/10.17270/J.LOG.2019.371
• 17. Lapierre, S. D., & Ruiz, A. B., 2007, Scheduling logistic activities to improve hospital supply systems. Computers & Operations Research, 34(3), 624-641. https://doi.org/10.1016/j.cor.2005.03.017
• 18. Lee, C. H., & Yoon, H.-J., 2017, Medical big data: promise and challenges. Kidney research and clinical practice, 36(1), 3. https://doi.org/10.23876/j.krcp.2017.36.1.3
• 19. Majchrzak-Lepczyk, J., & Bober, B., 2016, Selected aspects of the logistics network of public hospitals in the competitive market of health services. Logforum, 12(4), 6. https://doi.org/10.17270/J.LOG.2016.4.6
• 20. Raghupathi, W., & Raghupathi, V., 2014, Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3. https://doi.org/10.1186/2047-2501-2-3
• 21. Setzler III, H. H., 2007, Developing an accurate forecasting model for temporal and spatial ambulance demand via artificial neural networks: A comparative study of existing forecasting techniques vs. an artificial neural network, The University of North Carolina at Charlotte.
• 22. Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á., 2019, Decision-Making based on Big Data Analytics for People Management in Healthcare Organizations. Journal of Medical Systems, 43(9), 290. https://doi.org/10.1007/s10916-019-1419-x
• 23. Tlili, T., Abidi, S., & Krichen, S., 2018, A mathematical model for efficient emergency transportation in a disaster situation. The American journal of emergency medicine, 36(9), 1585-1590. https://doi.org/10.1016/j.ajem.2018.01.039
• 24. von Elmbach, A. F., Scholl, A., & Walter, R., 2019, Minimizing the maximal ergonomic burden in intra-hospital patient transportation. European Journal of Operational Research, 276(3), 840-854. https://doi.org/10.1016/j.ejor.2019.01.062
• 25. Wajid, S., Nezamuddin, N., & Unnikrishnan, A., 2020, Optimizing Ambulance Locations for Coverage Enhancement of Accident Sites in South Delhi. Transportation Research Procedia, 48, 280-289. https://doi.org/10.1016/j.trpro.2020.08.022
• 26. Yang, W., Su, Q., Huang, S. H., Wang, Q., Zhu, Y., & Zhou, M., 2019, Simulation modeling and optimization for ambulance allocation considering spatiotemporal stochastic demand. Journal of Management Science and Engineering, 4(4), 252-265. https://doi.org/10.1016/j.jmse.2020.01.004
• 27. Yang, Y., Cao, M., Cheng, L., Zhai, K., Zhao, X., & De Vos, J., 2021, Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study. Transportation Research Interdisciplinary Perspectives, 11, 100450. https://doi.org/10.1016/j.trip.2021.100450

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).