Using decision tree algorithms for estimating ICU admission of COVID-19 patients
Introduction: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required resources is crucial. This study aimed to develop an...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914822000685 |
| _version_ | 1818201043008225280 |
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| author | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi |
| author_facet | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi |
| author_sort | Mostafa Shanbehzadeh |
| collection | DOAJ |
| description | Introduction: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required resources is crucial. This study aimed to develop and compare models for early predicting ICU admission in COVID-19 patients at the point of hospital admission. Materials and methods: Using a single-center registry, we studied the records of 512 COVID-19 patients. First, the most important variables were identified using Chi-square test (at p < 0.01) and logistic regression (with odds ratio at P < 0.05). Second, we trained seven decision tree (DT) algorithms (decision stump (DS), Hoeffding tree (HT), LMT, J-48, random forest (RF), random tree (RT) and REP-Tree) using the selected variables. Finally, the models’ performance was evaluated. Furthermore, we used an external dataset to validate the prediction models. Results: Using the Chi-square test, 20 important variables were identified. Then, 12 variables were selected for model construction using logistic regression. Comparing the DT methods demonstrated that J-48 (F-score of 0.816 and AUC of 0.845) had the best performance. Also, the J-48 (F-score = 80.9% and AUC = 0.822) gained the best performance in generalizability using the external dataset. Conclusions: The study results demonstrated that DT algorithms can be used to predict ICU admission requirements in COVID-19 patients based on the first time of admission data. Implementing such models has the potential to inform clinicians and managers to adopt the best policy and get prepare during the COVID-19 time-sensitive and resource-constrained situation. |
| first_indexed | 2024-12-12T02:47:16Z |
| format | Article |
| id | doaj.art-22b92b5c5c2a4ee8a956413380f0b2e9 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-12T02:47:16Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-22b92b5c5c2a4ee8a956413380f0b2e92022-12-22T00:40:58ZengElsevierInformatics in Medicine Unlocked2352-91482022-01-0130100919Using decision tree algorithms for estimating ICU admission of COVID-19 patientsMostafa Shanbehzadeh0Raoof Nopour1Hadi Kazemi-Arpanahi2Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, IranDepartment of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, IranDepartment of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran; Department of Student Research Committee, Abadan University of Medical Sciences, Iran; Corresponding author. Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran.Introduction: Coronavirus disease 2019 (COVID-19) outbreak has overwhelmed many healthcare systems worldwide and put them at the edge of collapsing. As intensive care unit (ICU) capacities are limited, deciding on the proper allocation of required resources is crucial. This study aimed to develop and compare models for early predicting ICU admission in COVID-19 patients at the point of hospital admission. Materials and methods: Using a single-center registry, we studied the records of 512 COVID-19 patients. First, the most important variables were identified using Chi-square test (at p < 0.01) and logistic regression (with odds ratio at P < 0.05). Second, we trained seven decision tree (DT) algorithms (decision stump (DS), Hoeffding tree (HT), LMT, J-48, random forest (RF), random tree (RT) and REP-Tree) using the selected variables. Finally, the models’ performance was evaluated. Furthermore, we used an external dataset to validate the prediction models. Results: Using the Chi-square test, 20 important variables were identified. Then, 12 variables were selected for model construction using logistic regression. Comparing the DT methods demonstrated that J-48 (F-score of 0.816 and AUC of 0.845) had the best performance. Also, the J-48 (F-score = 80.9% and AUC = 0.822) gained the best performance in generalizability using the external dataset. Conclusions: The study results demonstrated that DT algorithms can be used to predict ICU admission requirements in COVID-19 patients based on the first time of admission data. Implementing such models has the potential to inform clinicians and managers to adopt the best policy and get prepare during the COVID-19 time-sensitive and resource-constrained situation.http://www.sciencedirect.com/science/article/pii/S2352914822000685COVID-19CoronavirusMachine learningIntensive care unitDecision tree |
| spellingShingle | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi Using decision tree algorithms for estimating ICU admission of COVID-19 patients Informatics in Medicine Unlocked COVID-19 Coronavirus Machine learning Intensive care unit Decision tree |
| title | Using decision tree algorithms for estimating ICU admission of COVID-19 patients |
| title_full | Using decision tree algorithms for estimating ICU admission of COVID-19 patients |
| title_fullStr | Using decision tree algorithms for estimating ICU admission of COVID-19 patients |
| title_full_unstemmed | Using decision tree algorithms for estimating ICU admission of COVID-19 patients |
| title_short | Using decision tree algorithms for estimating ICU admission of COVID-19 patients |
| title_sort | using decision tree algorithms for estimating icu admission of covid 19 patients |
| topic | COVID-19 Coronavirus Machine learning Intensive care unit Decision tree |
| url | http://www.sciencedirect.com/science/article/pii/S2352914822000685 |
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