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Study of Variables Influencing LOS with Machine Learning in Patients with Kidney Disease

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Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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Abstract

Kidney disease is a very important disease in the hospital context, in fact, the kidneys play a fundamental role for the whole body by maintaining the homeostasis of water for removing excess fluid and toxic substances. Therefore, it is necessary to assess all possible comorbidities of the patient before kidney surgery and possible complications. It is therefore necessary to analyze the length of stay (LOS) for proper hospital planning to ensure efficient and effective services. In this work the Length of Stay (LOS) of 70 patients of the AORN “A. Cardarelli” Hospital in Naples in the years 2019–2021 were analyzed. Machine Learning techniques were applied, and the algorithms implemented were Decision Tree, Random Forest and Naïve Bayes.

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References

  1. Levey, A.S., Coresh, J.: Chronic kidney disease. Lancet 379(9811), 165–180 (2012). https://doi.org/10.1016/S0140-6736(11)60178-5. ISSN 0140-6736

  2. Webster, A.C., Nagler, E.V., Morton, R.L., Masson, P.: Chronic kidney disease. Lancet 389(10075), 1238–1252 (2017). https://doi.org/10.1016/S0140-6736(16)32064-5. ISSN 0140-6736

  3. Levey, A.S., Coresh, J.: Chronic kidney disease. Lancet Lond. Engl. 379, 165–180 (2012)

    Article  Google Scholar 

  4. Fraser, S.D., Blakeman, T.: Chronic kidney disease: identification and management in primary care. Pragmatic Obs. Res. 7, 21–32 (2016)

    Article  Google Scholar 

  5. Mallappallil, M., Friedman, E.A., Delano, B.G., McFarlane, S.I., Salifu, M.O.: Chronic kidney disease in the elderly: evaluation and management. Clin. Pract. (Lond.) 11(5), 525–535 (2014). https://doi.org/10.2217/cpr.14.46

    Article  CAS  PubMed  Google Scholar 

  6. Di Lullo, L., House, A., Gorini, A., Santoboni, A., Russo, D., Ronco, C.: Chronic kidney disease and cardiovascular complications. Heart Fail. Rev. 20(3), 259–272 (2014). https://doi.org/10.1007/s10741-014-9460-9

    Article  CAS  Google Scholar 

  7. Tonelli, M., et al.: Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am. J. Transplant. 11, 2093–2109 (2011)

    Article  CAS  PubMed  Google Scholar 

  8. McAdams-DeMarco, M.A., et al.: Frailty, length of stay, and mortality in kidney transplant recipients: a national registry and prospective cohort study. Ann. Surg. 266(6), 1084–1090 (2017). https://doi.org/10.1097/SLA.0000000000002025. PMID: 27655240; PMCID: PMC5360544

    Article  PubMed  Google Scholar 

  9. Balato, G., Rizzo, M., Ascione, T., Smeraglia, F., Mariconda, M.: Re-infection rates and clinical outcomes following arthrodesis with intramedullary nail and external fixator for infected knee prosthesis: a systematic review and meta-analysis. BMC Musculoskelet. Disord. 19(1), 361 (2018)

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ascione, T., et al.: Clinical and microbiological outcomes in haematogenous spondylodiscitis treated conservatively. Eur. Spine J. 26(4), 489–495 (2017). https://doi.org/10.1007/s00586-017-5036-4

    Article  PubMed  Google Scholar 

  11. Balato, G., et al.: Hip and knee section, prevention, surgical technique: proceedings of international consensus on orthopedic infections. J. Arthroplasty 34(2S), S301–S307 (2019)

    Google Scholar 

  12. Smeraglia, F., Mariconda, M., Balato, G., Di Donato, S.L., Criscuolo, G., Maffulli, N.: Dubious space for Artelon joint resurfacing for basal thumb (trapeziometacarpal joint) osteoarthritis. A systematic review. Br. Med. Bull. 126(1), 79–84 (2018)

    Article  PubMed  Google Scholar 

  13. Kazmi, W.H., Obrador, G.T., Khan, S.S., Pereira, B.J.G., Kausz, A.T.: Late nephrology referral and mortality among patients with end-stage renal disease: a propensity score analysis. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial Transpl. Assoc. Eur. Ren. Assoc. 19, 1808–1814 (2004)

    Google Scholar 

  14. Bradbury, R.C., Golec, J.H., Steen, P.M.: Linking health outcomes and resource efficiency for hospitalized patients: do physicians with low mortality and morbidity rates also have low resource expenditures? Health Serv. Manag. Res. 13(1), 57–68 (2000)

    Article  CAS  Google Scholar 

  15. Brownell, M.D., Roos, N.P.: Variation in length of stay as a measure of efficiency in Manitoba hospitals. Can. Med. Assoc. J. 152(5), 675–682 (1995)

    CAS  Google Scholar 

  16. Trunfio, T.A., et al.: Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy. BMC Med. Inform. Decis. Mak. 22(1), 1–8 (2022)

    Google Scholar 

  17. Trunfio, T.A., Borrelli, A., Improta, G.: Is it possible to predict the length of stay of patients undergoing hip-replacement surgery? Int. J. Env. Res. Public Health 19(10), 6219 (2022)

    Google Scholar 

  18. Improta, G., Converso, G., Murino, T., Gallo, M., Perrone, A., Romano, M: Analytic hierarchy process (AHP) in dynamic configuration as a tool for health technology assessment (HTA): the case of biosensing optoelectronics in oncology. Int. J. Inf. Technol. Decis. Mak. 18(05), 1533–1550 (2019). https://doi.org/10.1142/S0219622019500263

  19. Improta, G., Simone, T., Bracale, M.: HTA (Health Technology Assessment): a means to reach governance goals and to guide health politics on the topic of clinical risk management. In: Dössel, O., Schlegel, W.C. (eds.) World Congress on Medical Physics and Biomedical Engineering, vol. 25/12. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03893-8_47

  20. Improta, G., et al.: An innovative contribution to health technology assessment. In: Ding, W., Jiang, H., Ali, M., Li, M. (eds.) Modern Advances in Intelligent Systems and Tools, pp. 127–131. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30732-4_16

  21. Cesarelli, G., Montella, E., Scala, A., Raiola, E., Triassi, M., Improta, G.: DMAIC approach for the reduction of healthcare-associated infections in the neonatal intensive care unit of the University Hospital of Naples ‘Federico II.’ In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds.) EMBEC 2020. IP, vol. 80, pp. 414–423. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64610-3_48

    Chapter  Google Scholar 

  22. Improta, G., Scala, A., Trunfio, T.A., Guizzi, G.: Application of supply chain management at drugs flow in an Italian Hospital District. J. Phys. Conf. Ser. 1828 (2020). International Symposium on Automation, Information and Computing (ISAIC 2020), 2–4 December 2020, Beijing, China. IOP Publishing Ltd. (2020)

    Google Scholar 

  23. Improta, Giovanni, et al.: Management of the diabetic patient in the diagnostic care pathway. In: Jarm, T., Cvetkoska, A., Mahnič-Kalamiza, S., Miklavcic, D. (eds.) EMBEC 2020. IP, vol. 80, pp. 784–792. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64610-3_88

    Chapter  Google Scholar 

  24. Bonavolontà, P., et al.: Postoperative complications after removal of pleomorphic adenoma from the parotid gland: a long-term follow up of 297 patients from 2002 to 2016 and a review of publications. Br. J. Oral Maxillofacial Surg. 57(10), 998–1002 (2019).https://doi.org/10.1016/j.bjoms.2019.08.008. ISSN 0266-4356

  25. Solari, D., et al.: Skull base reconstruction after endoscopic endonasal surgery: new strategies for raising the dam. In: 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT), pp. 28–32 (2019). https://doi.org/10.1109/METROI4.2019.8792878

  26. Maniscalco, G.T., et al.: Remission of early persistent cladribine-induced neutropenia after filgrastim therapy in a patient with relapsing-remitting multiple sclerosis. Multiple Sclerosis Relat. Disord. 43, 102151 (2020)

    Google Scholar 

  27. Esposito, C., Moscato, V., Sperlí, G.: Trustworthiness assessment of users in social reviewing systems. IEEE Trans. Syst. Man Cybern. Syst. 52(1), 151–165 (2022). https://doi.org/10.1109/TSMC.2020.3049082

    Article  Google Scholar 

  28. Sperlí, G.: A deep learning based chatbot for cultural heritage. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 935–937, March 2020. https://doi.org/10.1145/3341105.3374129

  29. Ianni, M., Masciari, E., Sperlí, G.: A survey of big data dimensions vs social networks analysis. J. Intell. Inf. Syst. 57(1), 73–100 (2020). https://doi.org/10.1007/s10844-020-00629-2

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sperlí, G.: A cultural heritage framework using a Deep Learning based Chatbot for supporting tourist journey. Expert Syst. Appl. 183, 115277 (2021). https://doi.org/10.1016/j.eswa.2021.115277

    Article  Google Scholar 

  31. Petrillo, A., Picariello, A., Santini, S., Scarciello, B., Sperli, G.: Model-based vehicular prognostics framework using big data architecture. Comput. Ind. 115, 103177 (2020). https://doi.org/10.1016/j.compind.2019.103177

    Article  Google Scholar 

  32. Sperlí, G.: A deep learning based community detection approach. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1107–1110, April 2019. https://doi.org/10.1145/3297280.3297574

  33. Mercorio, F., Mezzanzanica, M., Moscato, V., Picariello, A., Sperlí, G.: DICO: a graph-DB framework for community detection on big scholarly data. IEEE Trans. Emerg. Top. Comput. 9(4), 1987–2003 (2021). https://doi.org/10.1109/TETC.2019.2952765

    Article  Google Scholar 

  34. La Gatta, V., Moscato, V., Pennone, M., Postiglione, M., Sperlí, G.: Music recommendation via hypergraph embedding. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3146968

    Article  PubMed  Google Scholar 

  35. De Santo, A., Galli, A., Gravina, M., Moscato, V., Sperlì, G.: Deep learning for HDD health assessment: an application based on LSTM. IEEE Trans. Comput. 71(1), 69–80 (2020). https://doi.org/10.1109/TC.2020.3042053

    Article  Google Scholar 

  36. Guarino, F., Improta, G., Triassi, M., Castiglione, S., Cicatelli, A.: Air quality biomonitoring through Olea europaea L.: the study case of “Land of pyres”. Chemosphere 282, 131052. https://doi.org/10.1016/j.chemosphere.2021.131052

  37. Guarino, F., Improta, G., Triassi, M., Cicatelli, A., Castiglione, S.: Effects of zinc pollution and compost amendment on the root microbiome of a metal tolerant poplar clone. Front. Microbiol. 11, 1677 (2020). https://doi.org/10.3389/fmicb.2020.01677

    Article  PubMed  PubMed Central  Google Scholar 

  38. Guarino, F., et al.: Genetic characterization, micropropagation, and potential use for arsenic phytoremediation of Dittrichia viscosa (L.) Greuter. Ecotoxicol. Environ. Saf. 148, 675–683 (2018). https://doi.org/10.1016/j.ecoenv.2017.11.010

  39. Guarino, F., Cicatelli, A., Brundu, G., Improta, G., Triassi, M., Castiglione, S.: The use of MSAP reveals epigenetic diversity of the invasive clonal populations of Arundo donax L. PLoS One 14 (2019). https://doi.org/10.1371/journal.pone.0215096

  40. De Agostini, A., et al.: Heavy metal tolerance of orchid populations growing on abandoned mine tailings: a case study in Sardinia Island (Italy). Ecotoxicol. Environ. Saf. 189, 110018 (2020). https://doi.org/10.1016/j.ecoenv.2019.110018

    Article  CAS  PubMed  Google Scholar 

  41. Moccia, E., et al.: Use of Zea mays L. in phytoremediation of trichloroethylene. Environ. Sci. Pollut. Res. 24, 11053–11060 (2017). https://doi.org/10.1007/s11356-016-7570-8

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Authors and Affiliations

  1. Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy

    Teresa Angela Trunfio

  2. Department of Public Health, University of Naples “Federico II”, Naples, Italy

    Marta Rosaria Marino, Enrico Festa, Maria Triassi & Arianna Scala

  3. University of Rome “La Sapienza”, Rome, Italy

    Cristiana Giglio

  4. A.O.R.N. “Antonio Cardarelli”, Naples, Italy

    Massimo Majolo, Eliana Raiola & Giuseppe Longo

  5. Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy

    Maria Triassi

Authors
  1. Teresa Angela Trunfio
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Corresponding author

Correspondence to Marta Rosaria Marino .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Trunfio, T.A. et al. (2023). Study of Variables Influencing LOS with Machine Learning in Patients with Kidney Disease. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_57

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_57

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