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A comparison of different Machine Learning algorithms for predicting the length of hospital stay for patients undergoing cataract surgery

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Published:14 February 2022Publication History

ABSTRACT

The advancement of surgical techniques, the use of new drug therapies and the introduction of innovative medical devices have brought excellent results in all surgical disciplines, including Ophthalmology. This development, however, takes place in a difficult economic and financial context, especially for Italy, the reference country for this study. In this context, being able to obtain as standardized procedures as possible helps to provide a more appropriate response by maximizing the use of available resources. A parameter used in the literature is the Length of Stay (LOS). In this study, Machine Learning algorithms were used to build a classifier capable of predicting the total LOS of patients who undergone a surgery for the exportation of the natural crystalline lens with phacoemulsification starting from a set of independent variables. Random Forest proved to be the best algorithm for this application with an accuracy of over 90%.

References

  1. Erie JC, Baratz KH, Hodge DO, Schleck CD, Burke JP. Incidence of cataract surgery from 1980 through 2004: 25-year population-based study. J Cataract Refract Surg. 2007;33:1273–1277Google ScholarGoogle Scholar
  2. “ORGANIZATIONAL CLINICAL GUIDELINES ON CATARACT SURGERY”, Italian Ophthalmic Society. (2015)Google ScholarGoogle Scholar
  3. Ohrloff C, Zubcov AA. Comparison of phacoemulsification and planned extracapsular extraction. Ophthalmologica. 1997;211(1):8-12. doi: 10.1159/000310859. PMID: 8958525.Google ScholarGoogle Scholar
  4. Thanigasalam T, Reddy SC, Zaki RA. Factors Associated with Complications and Postoperative Visual Outcomes of Cataract Surgery; a Study of 1,632 Cases. J Ophthalmic Vis Res. 2015;10(4):375-384. doi:10.4103/2008-322X.158892Google ScholarGoogle Scholar
  5. Cedrone C, Culasso F, Cesareo M, Mancino R, Ricci F, Cupo G, Cerulli L. Prevalence and incidence of age-related cataract in a population sample from Priverno, Italy. Ophthalmic Epidemiol. 1999 Jun;6(2):95-103. doi: 10.1076/opep.6.2.95.1562. PMID: 10420209.Google ScholarGoogle Scholar
  6. Klein BE, Klein R, Lee KE. Diabetes, cardiovascular disease, selected cardiovascular disease risk factors, and the 5-year incidence of age-related cataract and progression of lens opacities: the Beaver Dam Eye Study. Am J Ophthalmol. 1998 Dec;126(6):782-90. doi: 10.1016/s0002-9394(98)00280-3. PMID: 9860001.Google ScholarGoogle Scholar
  7. Malot J, Combe C, Moss A, [Cost of cataract surgery in a public hospital]. Journal Francais D'ophtalmologie. 2011 Jan;34(1):10-16. DOI: 10.1016/j.jfo.2010.10.014.Google ScholarGoogle Scholar
  8. Keeffe, J.E. and Taylor, H.R. (1996), Cataract surgery in Australia 1985–94. Australian and New Zealand Journal of Ophthalmology, 24: 313-317. https://doi.org/10.1111/j.1442-9071.1996.tb01601.xGoogle ScholarGoogle ScholarCross RefCross Ref
  9. S. Shea, R. V. Sideli, W. DuMouchel, G. Pulver, R. R. Arons and P. D. Clayton, "Computer-generated informational messages directed to physicians: Effect on length of hospital stay", J. Amer. Med. Inf. Assoc., vol. 2, no. 1, pp. 58-64, Jan./Feb. 1995.Google ScholarGoogle Scholar
  10. J. Shen, R. Andersen, R. Brook, G. Kominski, P. S. Albert and N. Wenger, "The effects of payment method on clinical decision-making: Physician responses to clinical scenarios", Med. Care, vol. 42, no. 3, pp. 297-302, Mar. 2004.Google ScholarGoogle Scholar
  11. Santini, S., Pescapé, A., Valente, A. S., Abate, V., Improta, G., Triassi, M., ... & Filosa, A. (2017, July). Using fuzzy logic for improving clinical daily-care of β-thalassemia patients. In Fuzzy Systems (FUZZ-IEEE), 2017 IEEE International Conference (pp. 1-6). IEEE.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Improta, G., Mazzella, V., Vecchione, D., Santini, S., & Triassi, M. (2020). Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients. Journal of evaluation in clinical practice, 26(4), 1224-1234.Google ScholarGoogle ScholarCross RefCross Ref
  13. Stineman MG, Williams SV. Predicting inpatient rehabilitation length of stay. Archives of Physical Medicine and Rehabilitation. 1990 Oct;71(11):881-887.Google ScholarGoogle Scholar
  14. Trunfio T.A., Scala A., Vecchia A.D., Marra A., Borrelli A. (2021) Multiple Regression Model to Predict Length of Hospital Stay for Patients Undergoing Femur Fracture Surgery at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital. In: Jarm T., Cvetkoska A., Mahnič-Kalamiza S., Miklavcic D. (eds) 8th European Medical and Biological Engineering Conference. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_94Google ScholarGoogle ScholarCross RefCross Ref
  15. Improta, G., Triassi, M., Guizzi, G., Santillo, L. C., Revetria, R., Catania, A., & Cassettari, L. (2012). An innovative contribution to health technology assessment. In Modern Advances in Intelligent Systems and Tools, 431, 127-131. Springer Berlin Heidelberg.Google ScholarGoogle Scholar
  16. Improta G, Perrone A, Russo MA, Triassi M (2019). Health technology assessment (HTA) of optoelectronic biosensors for oncology by analytic hierarchy process (AHP) and Likert scale. BMC Med Res Methodol. 2019 Jul 5;19(1):140. doi: 10.1186/s12874-019-0775-z.Google ScholarGoogle Scholar
  17. Ponsiglione, A. M., Ricciardi, C., Improta, G., Orabona, G. D. A., Sorrentino, A., Amato, F., & Romano, M. (2021). A Six Sigma DMAIC methodology as a support tool for Health Technology Assessment of two antibiotics. Mathematical Biosciences and Engineering, 18(4), 3469-3490.Google ScholarGoogle Scholar
  18. Improta, G., Ponsiglione, A. M., Parente, G., Romano, M., Cesarelli, G., Rea, T., ... & Triassi, M. (2020, November). Evaluation of medical training courses satisfaction: Qualitative analysis and analytic hierarchy process. In European Medical and Biological Engineering Conference (pp. 518-526). Springer, Cham.Google ScholarGoogle Scholar
  19. Mandahawi, N. , Al-Araidah, O. , Boran, A. , Khasawneh, M. (2011). ‘Application of lean Six Sigma tools to minimise length of stay for ophthalmology day case surgery’. International Journal of Six Sigma and Competitive Advantage. 6, 3, 156-172Google ScholarGoogle ScholarCross RefCross Ref
  20. Scala, A., Ponsiglione, A. M., Loperto, I., Della Vecchia, A., Borrelli, A., Russo, G., ... & Improta, G. (2021). Lean six sigma approach for reducing length of hospital stay for patients with femur fracture in a university hospital. International Journal of Environmental Research and Public Health, 18(6), 2843.Google ScholarGoogle Scholar
  21. Latessa, I., Fiorillo, A., Picone, I., Balato, G., Trunfio, T.A., Scala, A. and Triassi, M. (2021), "Implementing fast track surgery in hip and knee arthroplasty using the lean Six Sigma methodology", The TQM Journal, Vol. 33 No. 7, pp. 131-147. https://doi.org/10.1108/TQM-12-2020-0308Google ScholarGoogle ScholarCross RefCross Ref
  22. Mandahawi, N., Shurrab, M., Al-Shihabi, S., Abdallah, A. A., & Alfarah, Y. M. (2017). Utilizing six sigma to improve the processing time: a simulation study at an emergency department. Journal of Industrial and Production Engineering, 34(7), 495-503.Google ScholarGoogle ScholarCross RefCross Ref
  23. Roy, S., Prasanna Venkatesan, S., & Goh, M. (2020). Healthcare services: A systematic review of patient-centric logistics issues using simulation. Journal of the Operational Research Society, 1-23.Google ScholarGoogle Scholar
  24. Ricciardi, C., Ponsiglione, A. M., Converso, G., Santalucia, I., Triassi, M., & Improta, G. (2020). Implementation and validation of a new method to model voluntary departures from emergency departments. Running Title: Modeling Voluntary departures from emergency departments. Mathematical Biosciences and Engineering: MBE, 18(1), 253-273.Google ScholarGoogle Scholar
  25. Reindl, Sonja & Mönch, Lars & Monch, Maria & Scheider, Andreas. (2010). Modeling and simulation of cataract surgery processes. Proceedings - Winter Simulation Conference. 1937 - 1945. 10.1109/WSC.2009.5429212.Google ScholarGoogle Scholar
  26. Amin Karimi, Mohammad Mehdi Sepehri, Elham Yavari, A simulation model approach to decrease the length of stay of patients undergoing cataract surgery. Perioperative Care and Operating Room Management, Volume 21, 2020, 100133, ISSN 2405-6030, https://doi.org/10.1016/j.pcorm.2020.100133.Google ScholarGoogle Scholar
  27. Elalouf, A., Tsadikovich, D., & Hovav, S. (2021). A simulation-based approach for improving the clinical blood sample supply chain. Health Care Management Science, 1-18.Google ScholarGoogle Scholar
  28. Cesarelli, G., Scala, A., Vecchione, D., Ponsiglione, A. M., & Guizzi, G. (2021, February). An Innovative Business Model for a Multi-echelon Supply Chain Inventory Management Pattern. In Journal of Physics: Conference Series (Vol. 1828, No. 1, p. 012082). IOP Publishing.Google ScholarGoogle Scholar
  29. Deryahanoğlu, O., & Kocaoğlu, B. (2019). Applications of RFID systems in healthcare management: a simulation for emergency Department. International Journal of Innovative Technology and Exploring Engineering.Google ScholarGoogle ScholarCross RefCross Ref
  30. Improta, G., Ricciardi, C., Amato, F., D'Addio, G., Cesarelli, M., & Romano, M. (2019, September). Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography. In Mediterranean Conference on Medical and Biological Engineering and Computing (pp. 793-799). Springer, Cham.Google ScholarGoogle Scholar
  31. Morton, E. Marzban, G. Giannoulis, A. Patel, R. Aparasu and I. A. Kakadiaris, "A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients," 2014 13th International Conference on Machine Learning and Applications, 2014, pp. 428-431, doi: 10.1109/ICMLA.2014.76.Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Other conferences
            BECB 2021: 2021 International Symposium on Biomedical Engineering and Computational Biology
            August 2021
            262 pages
            ISBN:9781450384117
            DOI:10.1145/3502060

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            Publication History

            • Published: 14 February 2022

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