Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19
Abstract Background The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients’ length of stay (LOS) to optimize clinical care and utilization of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-022-07921-2 |
| _version_ | 1811302442055237632 |
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| author | Azam Orooji Mostafa Shanbehzadeh Esmat Mirbagheri Hadi Kazemi-Arpanahi |
| author_facet | Azam Orooji Mostafa Shanbehzadeh Esmat Mirbagheri Hadi Kazemi-Arpanahi |
| author_sort | Azam Orooji |
| collection | DOAJ |
| description | Abstract Background The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients’ length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients’ LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. Methods Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models’ performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. Results After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients’ LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. Conclusions MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions. |
| first_indexed | 2024-04-13T07:27:52Z |
| format | Article |
| id | doaj.art-785a499bc0174a44b4acff0c2e65e2cd |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-04-13T07:27:52Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-785a499bc0174a44b4acff0c2e65e2cd2022-12-22T02:56:26ZengBMCBMC Infectious Diseases1471-23342022-12-0122111310.1186/s12879-022-07921-2Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19Azam Orooji0Mostafa Shanbehzadeh1Esmat Mirbagheri2Hadi Kazemi-Arpanahi3Department of Medical Informatics, Department of Advanced Technologies, School of Medicine, North Khorasan University of Medical Science (NKUMS)Department of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical SciencesDepartment of Health Information Management, Iran University of Medical SciencesDepartment of Health Information Management, Department of Health Information Technology, Abadan University of Medical SciencesAbstract Background The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients’ length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients’ LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS. Methods Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models’ performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models. Results After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients’ LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively. Conclusions MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.https://doi.org/10.1186/s12879-022-07921-2COVID-19CoronavirusArtificial neural networksTraining algorithmsLength of stay |
| spellingShingle | Azam Orooji Mostafa Shanbehzadeh Esmat Mirbagheri Hadi Kazemi-Arpanahi Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 BMC Infectious Diseases COVID-19 Coronavirus Artificial neural networks Training algorithms Length of stay |
| title | Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 |
| title_full | Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 |
| title_fullStr | Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 |
| title_full_unstemmed | Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 |
| title_short | Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19 |
| title_sort | comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with covid 19 |
| topic | COVID-19 Coronavirus Artificial neural networks Training algorithms Length of stay |
| url | https://doi.org/10.1186/s12879-022-07921-2 |
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