Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP)
Britta Marko, Lars Palmowski, Hartmuth Nowak, Andrea Witowski, Björn Koos, Katharina Rump, Lars Bergmann, Julia Bandow, Martin Eisenacher, Patrick Günther, Michael Adamzik, Barbara Sitek, Tim Rahmel · 2024
Read Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP) by Britta Marko, Lars Palmowski, Hartmuth Nowak, Andrea Witowski, Björn Koos, Katharina Rump, Lars Bergmann, Julia Bandow, Martin Eisenacher, Patrick Günther, Michael Adamzik, Barbara Sitek, Tim Rahmel (2024) 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-11647398.txt.
Description
Britta Marko and co-authors lay out the plan for the KI.SEP study in this 2024 protocol paper. The study asks whether machine learning can set better first doses of antibiotics for patients with sepsis in intensive care. Blood-level checks are the current standard, yet they cannot guide the very first dose and are missing in many hospitals.
The plan enrolls two hundred patients in a German university hospital, half given one common antibiotic and half another. Blood samples and clinical data will be gathered over the first eight days and again at day thirty. Two model families will be built: one using routine ward data, and one adding richer lab and protein data. The long-term aim is software that sits in hospital systems and suggests a personal dose for each patient.
Catalog record
| CREATOR | Britta Marko, Lars Palmowski, Hartmuth Nowak, Andrea Witowski, Björn Koos, Katharina Rump, Lars Bergmann, Julia Bandow, Martin Eisenacher, Patrick Günther, Michael Adamzik, Barbara Sitek, Tim Rahmel |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 35 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |