Correction: Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3
| Author | Rahman, Md Sohanur |
| Author | Islam, Khandaker Reajul |
| Author | Prithula, Johayra |
| Author | Kumar, Jaya |
| Author | Mahmud, Mufti |
| Author | Alam, Mohammed Fasihul |
| Author | Reaz, Mamun Bin Ibne |
| Author | Alqahtani, Abdulrahman |
| Author | Chowdhury, Muhammad E.H. |
| Available date | 2024-11-05T09:31:44Z |
| Publication Date | 2024-09-27 |
| Publication Name | BMC medical informatics and decision making |
| Identifier | http://dx.doi.org/10.1186/s12911-024-02685-y |
| Citation | Rahman, M. S., Islam, K. R., Prithula, J., Kumar, J., Mahmud, M., Alam, M. F., ... & Chowdhury, M. E. (2024). Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3. BMC medical informatics and decision making, 24(1), 249. |
| Abstract | Background Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. |
| Language | en |
| Publisher | Springer Nature link |
| Subject | Machine |
| Type | Article |
| Issue Number | 1 |
| Volume Number | 24 |
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