Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review
importance 7/10
ai assisted
Sepsis Diagnosis and Treatment critical care stage: research mixed
Problem
Sepsis is life-threatening and early detection is difficult; delays significantly worsen patient outcomes.
Why it matters
Sepsis can lead to organ failure and death, making earlier detection clinically critical.
What the AI did
This is a systematic review synthesizing 80 studies of ML/DL approaches (e.g., LSTM, transformers, ensembles) for early sepsis prediction, mainly in ICU settings.
Results
80 studies included; no model performance metrics stated in abstract
Impact
Synthesizes existing ML/DL sepsis-prediction research to inform future work.
Record
| Venue | BMC Infectious Diseases |
|---|---|
| Published | 2025 |
| Work type | review |
| Authors | Muhammad Zubair, Irfanud Din, Nadeem Sarwar, Elov Botir Boltayevich, Sаmаriddin Mаkhmudov, Zouheir Trabelsi |
| Citations | 29 (OpenAlex, at import time) |
| References | 98 |
| Model or method | Reviewed models include LSTM, transformers, ensemble methods |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s12879-025-11423-2 |
| Open access | gold |
| Keywords | sepsis, machine learning, deep learning, systematic review, ICU, early detection |
Importance rating
7
significant — thousands to millions affected
Abstract states sepsis is life-threatening, causes organ failure and death, and early detection is vital; review covers 80 studies, indicating major clinical impact.