Enhancing the accuracy and effectiveness of diagnosis of spontaneous bacterial peritonitis in cirrhotic patients: A machine learning approach utilizing clinical and laboratory data
Our analysis highlights the potential of machine learning to enhance the accuracy of SBP diagnosis in cirrhotic patients. Integrating these models into clinical workflows could substantially improve patient outcomes. To achieve this, ongoing multidisciplinary research is crucial. Ensuring model interpretability, continuous monitoring, and rigorous validation will be essential for the successful implementation of real-time clinical decision support systems.
Authors
- Ali Jahanian (ORCID: https://orcid.org/0000-0002-8580-3560)
- Mobin Fathy (ORCID: https://orcid.org/0000-0001-6624-2342)
- Somayye Taghvaei (ORCID: https://orcid.org/0000-0002-4282-1224)
- Babak Khorsand (ORCID: https://orcid.org/0000-0002-1585-3909)
- Mohsen Rajabnia (ORCID: https://orcid.org/0000-0002-3123-4315)
- Hamidreza Houri (ORCID: https://orcid.org/0000-0002-6813-0229)
Institutions
- University of California, Irvine (US)
- Jahrom University of Medical Sciences (IR)
- National Institute of Genetic Engineering and Biotechnology (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- Advances in Medical Sciences
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1016/j.advms.2024.10.001
- Citations
- 17
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 2.04
Funders
- Shahid Beheshti University of Medical Sciences
- Research Institute for Gastroenterology and Liver Diseases