Machine Learning Approach to Classify Vibrio vulnificus Necrotizing Fasciitis, Non-Vibrio Necrotizing Fasciitis and Cellulitis
Chia-Peng Chang, Kai-Hsiang Wu · 2024
Read Machine Learning Approach to Classify Vibrio vulnificus Necrotizing Fasciitis, Non-Vibrio Necrotizing Fasciitis and Cellulitis by Chia-Peng Chang, Kai-Hsiang Wu (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-11646401.txt.
Description
Chia-Peng Chang and Kai-Hsiang Wu built a computer model that helps doctors tell three skin and tissue infections apart, in a 2024 study. The three — cellulitis, flesh-eating infection from other germs, and the same illness caused by Vibrio vulnificus — can look alike at first. Telling them apart fast matters, because the Vibrio form can turn deadly in a short time.
The team trained a learning model called LightGBM on records from 180 hospital patients with soft tissue infections. They split the data five ways to check that the model held up on fresh cases. A tool that flags the Vibrio form early could speed up care and cut the risk of a wrong first call. The work shows how machine learning can back up hard bedside choices.
Catalog record
| CREATOR | Chia-Peng Chang, Kai-Hsiang Wu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |