Clinical Research
Year 2023,
, 500 - 6, 18.09.2023
Abstract
References
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- 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.
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- 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.
Year 2023,
, 500 - 6, 18.09.2023
Abstract
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.
References
- 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.
There are 25 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Internal Diseases |
| Journal Section | Original Articles |
| Authors | |
| Early Pub Date | July 14, 2023 |
| Publication Date | September 18, 2023 |
| Acceptance Date | May 16, 2023 |
| Published in Issue | Year 2023 |
Chief Editors
Assoc. Prof. Zülal Öner
İzmir Bakırçay University, Department of Anatomy, İzmir, Türkiye
Assoc. Prof. Deniz Şenol
Düzce University, Department of Anatomy, Düzce, Türkiye
Editors
Assoc. Prof. Serkan Öner
İzmir Bakırçay University, Department of Radiology, İzmir, Türkiye
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