Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies
Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa · 2024
Read Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies by Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa (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-11639816.txt.
Description
Santino Nanini and five co-authors build smart models to grade low blood oxygen in mass harm events. Such events can flood triage teams, and early grading saves lives. Models trained on large hospital data sets used body signals from pro grade sensors.
Tree based models beat sequence models on speed, clarity, and trust for live use. Six body signals stood out as most useful, drawn from a known early warning score. Group voting models gave tiny gains but no clear win. The team plans tests with data from more hospitals to widen use.
Catalog record
| CREATOR | Santino Nanini, Mariem Abid, Yassir Mamouni, Arnaud Wiedemann, Philippe Jouvet, Stephane Bourassa |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |