Klinik Araştırma
BibTex RIS Kaynak Göster
Yıl 2023, Cilt: 5 Sayı: 3, 500 - 6, 18.09.2023
https://doi.org/10.37990/medr.1226429

Öz

Kaynakça

  • 1. Akcan FA, Onec K, Annakkaya A, et al. Düzce University Hospital in the pandemic process: from the perspective of chief physician. Konuralp Medical Journal. 2020;12:354–7. 2. Costa GJ, Júnior H de AF, Malta FC, et al.
  • 2. The impact of the COVID-19 pandemic on tertiary care cancer center: Analyzing administrative data. Semin Oncol. 2022;49:182– 8.
  • 3. Abdollahi F, Ghanyan S, Asadi F. COVID-19 pandemic and management on hospital length of stay: A review. Healthcare in Low-Resource Settings. 2021;9:10057.
  • 4. Scanlon C, Cheng R, McRobb E, Ibrahim M. In-house testing for COVID-19: effects on length of stay, isolation and the need for inpatient rehabilitation. Aust Health Review. 2022;46:273–8. 5. Alwafi H, Naser AY, Qanash S, et al.
  • 5. Predictors of length of hospital stay, mortality, and outcomes among hospitalised covid-19 patients in Saudi Arabia: a cross-sectional study. J Multidiscip Healthc. 2021;14:839-52. 6. Javaid M, Haleem A, Pratap Singh R, et al. Significance of machine learning in healthcare: features, pillars and applications. International Journal of Intelligent Networks. 2022;3:58-73.
  • 7. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14:337-9.
  • 8. WHO European health information at your fingertips. https://gateway.euro.who.int/en/indicators/ hfa_540-6100-average-length-of-stay-all-hospitals/ visualizations/#id=19635&tab=table access date 04.05.2023.
  • 9. Zayed NE, Bessar MA, Lutfy S. CO-RADS versus CT-SS scores in predicting severe COVID-19 patients: retrospective comparative study. Egypt J Bronchol. 2021;15:13.
  • 10. The jamovi project. https://www.jamovi.org access date 25.12.2022 11. R: A Language and environment for statistical computing. https://cran.r-project.org access date 25.12.2022
  • 12. A Short Introduction to the caret package. https://cran.rproject.org/web/packages/caret/vignettes/caret.html acces date 06.12.2022. 13. Kumar A. Pre-processing and modelling using caret package in R. International Journal of Computer Applications. 2018;181:39–42.
  • 14. Al-Areqi F, Konyar MZ. Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study. Biomed Signal Process Control. 2022;76:103662.
  • 15. Raschka S. Model evaluation, model selection, and algorithm selection in machine learning. ArXiv. 2020:/abs/1811.12808
  • 16. Kuhn M. Building predictive models in R using the caret package. Journal of Statistical Software. 2008;28:1–26.
  • 17. Bond RR, Novotny T, Andrsova I, et al. Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol. 2018;51:6–11.
  • 18. Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: measures of agreement. Perspect Clin Res. 2017;8:187.
  • 19. Grandini M, Bagli E, Visani G. Metrics for multi-class classification: an overview. ArXiv. 2020:/abs/2008.05756
  • 20. Dalianis H. Evaluation Metrics and Evaluation. In: Dalianis H, editor. Clinical Text Mining: Secondary Use of Electronic Patient Records. Cham: Springer International Publishing; 2018. p. 45–53.
  • 21. Alimohamadi Y, Yekta EM, Sepandi M, et al. Hospital length of stay for COVID-19 patients: a systematic review and meta-analysis. Multidiscip Respir Med. 2022;17:856.
  • 22. Savrun A, Aydin IE, Savrun ST, Karaman U. The predictive role of biomarkers for mortality in COVID-19 patients. Trop Biomed. 2021;38:366–70.
  • 23. Oksuz E, Malhan S, Gonen MS, et al. COVID-19 healthcare cost and length of hospital stay in Turkey: retrospective analysis from the first peak of the pandemic. Health Econ Rev. 2021;11:39.
  • 24. Chamberlin JH, Aquino G, Schoepf UJ, et al. An interpretable chest CT deep learning algorithm for quantification of COVID-19 lung disease and prediction of inpatient morbidity and mortality. Acad Radiol. 2022;29:1178–88.
  • 25. Purkayastha S, Xiao Y, Jiao Z, et al. Machine learning-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean J Radiol. 2021;22:1213–24.
  • 26. Olivato M, Rossetti N, Gerevini AE, et al. Machine learning models for predicting short-long length of stay of COVID-19 patients. Procedia Comput Sci. 2022;207:1232–41.
  • 27. Saadatmand S, Salimifard K, Mohammadi R, et al. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Ann Oper Res. 2022;1-29.
  • 28. Alabbad DA, Almuhaideb AM, Alsunaidi SJ, et al. Machine learning model for predicting the length of stay in the intensive care unit for COVID-19 patients in the eastern province of Saudi Arabia. Inform Med Unlocked. 2022;30:100937.

Prediction of Short or Long Length of Stay COVID-19 by Machine Learning

Yıl 2023, Cilt: 5 Sayı: 3, 500 - 6, 18.09.2023
https://doi.org/10.37990/medr.1226429

Öz

Aim: The aim of this study is to utilize machine learning techniques to accurately predict the length of stay for Covid-19 patients, based on basic clinical parameters.
Material and Methods: The study examined seven key variables, namely age, gender, length of hospitalization, c-reactive protein,
ferritin, lymphocyte count, and the COVID-19 Reporting and Data System (CORADS), in a cohort of 118 adult patients who were
admitted to the hospital with a diagnosis of Covid-19 during the period of November 2020 to January 2021. The data set is partitioned into a training and validation set comprising 80% of the data and a test set comprising 20% of the data in a random manner. The present study employed the caret package in the R programming language to develop machine learning models aimed at predicting the length of stay (short or long) in a given context. The performance metrics of these models were subsequently documented.
Results: The k-nearest neighbor model produced the best results among the various models. As per the model, the evaluation
outcomes for the estimation of hospitalizations lasting for 5 days or less and those exceeding 5 days are as follows: The accuracy
rate was 0.92 (95% CI, 0.73-0.99), the no-information rate was 0.67, the Kappa rate was 0.82, and the F1 score was 0.89 (p=0.0048).
Conclusion: By applying machine learning into Covid-19, length of stay estimates can be made with more accuracy, allowing for more effective patient management.

Kaynakça

  • 1. Akcan FA, Onec K, Annakkaya A, et al. Düzce University Hospital in the pandemic process: from the perspective of chief physician. Konuralp Medical Journal. 2020;12:354–7. 2. Costa GJ, Júnior H de AF, Malta FC, et al.
  • 2. The impact of the COVID-19 pandemic on tertiary care cancer center: Analyzing administrative data. Semin Oncol. 2022;49:182– 8.
  • 3. Abdollahi F, Ghanyan S, Asadi F. COVID-19 pandemic and management on hospital length of stay: A review. Healthcare in Low-Resource Settings. 2021;9:10057.
  • 4. Scanlon C, Cheng R, McRobb E, Ibrahim M. In-house testing for COVID-19: effects on length of stay, isolation and the need for inpatient rehabilitation. Aust Health Review. 2022;46:273–8. 5. Alwafi H, Naser AY, Qanash S, et al.
  • 5. Predictors of length of hospital stay, mortality, and outcomes among hospitalised covid-19 patients in Saudi Arabia: a cross-sectional study. J Multidiscip Healthc. 2021;14:839-52. 6. Javaid M, Haleem A, Pratap Singh R, et al. Significance of machine learning in healthcare: features, pillars and applications. International Journal of Intelligent Networks. 2022;3:58-73.
  • 7. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14:337-9.
  • 8. WHO European health information at your fingertips. https://gateway.euro.who.int/en/indicators/ hfa_540-6100-average-length-of-stay-all-hospitals/ visualizations/#id=19635&tab=table access date 04.05.2023.
  • 9. Zayed NE, Bessar MA, Lutfy S. CO-RADS versus CT-SS scores in predicting severe COVID-19 patients: retrospective comparative study. Egypt J Bronchol. 2021;15:13.
  • 10. The jamovi project. https://www.jamovi.org access date 25.12.2022 11. R: A Language and environment for statistical computing. https://cran.r-project.org access date 25.12.2022
  • 12. A Short Introduction to the caret package. https://cran.rproject.org/web/packages/caret/vignettes/caret.html acces date 06.12.2022. 13. Kumar A. Pre-processing and modelling using caret package in R. International Journal of Computer Applications. 2018;181:39–42.
  • 14. Al-Areqi F, Konyar MZ. Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study. Biomed Signal Process Control. 2022;76:103662.
  • 15. Raschka S. Model evaluation, model selection, and algorithm selection in machine learning. ArXiv. 2020:/abs/1811.12808
  • 16. Kuhn M. Building predictive models in R using the caret package. Journal of Statistical Software. 2008;28:1–26.
  • 17. Bond RR, Novotny T, Andrsova I, et al. Automation bias in medicine: the influence of automated diagnoses on interpreter accuracy and uncertainty when reading electrocardiograms. J Electrocardiol. 2018;51:6–11.
  • 18. Ranganathan P, Pramesh CS, Aggarwal R. Common pitfalls in statistical analysis: measures of agreement. Perspect Clin Res. 2017;8:187.
  • 19. Grandini M, Bagli E, Visani G. Metrics for multi-class classification: an overview. ArXiv. 2020:/abs/2008.05756
  • 20. Dalianis H. Evaluation Metrics and Evaluation. In: Dalianis H, editor. Clinical Text Mining: Secondary Use of Electronic Patient Records. Cham: Springer International Publishing; 2018. p. 45–53.
  • 21. Alimohamadi Y, Yekta EM, Sepandi M, et al. Hospital length of stay for COVID-19 patients: a systematic review and meta-analysis. Multidiscip Respir Med. 2022;17:856.
  • 22. Savrun A, Aydin IE, Savrun ST, Karaman U. The predictive role of biomarkers for mortality in COVID-19 patients. Trop Biomed. 2021;38:366–70.
  • 23. Oksuz E, Malhan S, Gonen MS, et al. COVID-19 healthcare cost and length of hospital stay in Turkey: retrospective analysis from the first peak of the pandemic. Health Econ Rev. 2021;11:39.
  • 24. Chamberlin JH, Aquino G, Schoepf UJ, et al. An interpretable chest CT deep learning algorithm for quantification of COVID-19 lung disease and prediction of inpatient morbidity and mortality. Acad Radiol. 2022;29:1178–88.
  • 25. Purkayastha S, Xiao Y, Jiao Z, et al. Machine learning-based prediction of COVID-19 severity and progression to critical illness using CT imaging and clinical data. Korean J Radiol. 2021;22:1213–24.
  • 26. Olivato M, Rossetti N, Gerevini AE, et al. Machine learning models for predicting short-long length of stay of COVID-19 patients. Procedia Comput Sci. 2022;207:1232–41.
  • 27. Saadatmand S, Salimifard K, Mohammadi R, et al. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Ann Oper Res. 2022;1-29.
  • 28. Alabbad DA, Almuhaideb AM, Alsunaidi SJ, et al. Machine learning model for predicting the length of stay in the intensive care unit for COVID-19 patients in the eastern province of Saudi Arabia. Inform Med Unlocked. 2022;30:100937.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İç Hastalıkları
Bölüm Özgün Makaleler
Yazarlar

Muhammet Özbilen 0000-0001-6052-7486

Zübeyir Cebeci 0000-0001-7862-4268

Aydın Korkmaz 0000-0001-7283-2795

Yasemin Kaya 0000-0001-7360-8090

Kaan Erbakan 0000-0002-5581-500X

Erken Görünüm Tarihi 14 Temmuz 2023
Yayımlanma Tarihi 18 Eylül 2023
Kabul Tarihi 16 Mayıs 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 5 Sayı: 3

Kaynak Göster

AMA Özbilen M, Cebeci Z, Korkmaz A, Kaya Y, Erbakan K. Prediction of Short or Long Length of Stay COVID-19 by Machine Learning. Med Records. Eylül 2023;5(3):500-6. doi:10.37990/medr.1226429

 Chief Editors

Assoc. Prof. Zülal Öner
Address: İzmir Bakırçay University, Department of Anatomy, İzmir, Turkey

Assoc. Prof. Deniz Şenol
Address: Düzce University, Department of Anatomy, Düzce, Turkey

E-mail: medrecsjournal@gmail.com

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