Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3
In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.
Authors
- Jaya Kumar (ORCID: https://orcid.org/0000-0002-8752-0708)
- Mufti Mahmud (ORCID: https://orcid.org/0000-0002-2037-8348)
- Md. Sohanur Rahman (ORCID: https://orcid.org/0009-0004-1976-9909)
- Muhammad E. H. Chowdhury (ORCID: https://orcid.org/0000-0003-0744-8206)
- Mamun Bin Ibne Reaz
- Abdulrahman Alqahtani (ORCID: https://orcid.org/0000-0002-6658-3240)
- Khandaker Reajul Islam (ORCID: https://orcid.org/0000-0003-2863-8156)
- M Fasihul Alam (ORCID: https://orcid.org/0000-0003-2590-851X)
- Johayra Prithula (ORCID: https://orcid.org/0009-0003-2178-5126)
Institutions
- Prince Sattam Bin Abdulaziz University (SA)
- Majmaah University (SA)
- University of Dhaka (BD)
- University Kebangsaan Malaysia Medical Centre (MY)
- Independent University, Bangladesh (BD)
- Nottingham Trent University (GB)
- Qatar University (QA)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-09-09
- DOI
- https://doi.org/10.1186/s12911-024-02655-4
- Citations
- 18
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.96
Funders
- Prince Sattam bin Abdulaziz University
- Qatar University