Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review
Mariana González Garcés, Juanita Valencia García, Camilo Jiménez Triana, Jerónimo Cárdenas Montoya, Erwin Hernando Hernández Rincón · 2026
Read Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review by Mariana González Garcés, Juanita Valencia García, Camilo Jiménez Triana, Jerónimo Cárdenas Montoya, Erwin Hernando Hernández Rincón (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13234702.txt.
Description
Mariana González Garcés and four co-authors wrote this 2026 review of smart models that aim to spot sepsis early in intensive care. Sepsis is a body-wide overreaction to infection, and hours matter. The team mapped the field by fixed review rules, gathering studies that built or tested prediction models for adults in intensive care.
Thirty-seven studies made the cut. Most trained their models on past ward records and leaned on machine learning, yet few tested them in new hospitals. Reported scores swung widely. Barely any study spoke to bedside use, plain explanation of the scores, or live workflow fit. The authors urge clearer methods and trials aimed at real use before such models earn trust at the bedside.
Catalog record
| CREATOR | Mariana González Garcés, Juanita Valencia García, Camilo Jiménez Triana, Jerónimo Cárdenas Montoya, Erwin Hernando Hernández Rincón |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |