Machine Learning–Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study
Eman Badran, Oraib Al-Smadi, Loiy T Algazo, Alaa T Al Ghazo, Arwa Al Anber, Areej Sharaqa, Lena Abu-Argoub, Shatha Al Jaberi, Abdulrahman Alhanbali, Taimein Yacoub, Shahd Rihan, Hala Al-Jaberi, Arriel Benis · 2026
Read Machine Learning–Based Prediction of Culture-Confirmed Neonatal Sepsis in a Tertiary Neonatal Intensive Care Unit: Retrospective Cohort Study by Eman Badran, Oraib Al-Smadi, Loiy T Algazo, Alaa T Al Ghazo, Arwa Al Anber, Areej Sharaqa, Lena Abu-Argoub, Shatha Al Jaberi, Abdulrahman Alhanbali, Taimein Yacoub, Shahd Rihan, Hala Al-Jaberi, Arriel Benis (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-13573726.txt.
Description
Badran and a large team built learned tools in twenty twenty-six to flag blood infection in new-born babies in a Jordan care unit. Such infection is a top cause of harm and death for the young in poor lands. Signs are vague and lab proof is slow, so early risk sorts are key.
They trained three tools on files of more than three thousand babies in one unit. The best tool hit high marks on a held test set for truth and catch rate. Blood marks for swell and clot cell count were top cues in its calls. Tree tools gave clear rules but mid scores, while a net tool lagged.
Catalog record
| CREATOR | Eman Badran, Oraib Al-Smadi, Loiy T Algazo, Alaa T Al Ghazo, Arwa Al Anber, Areej Sharaqa, Lena Abu-Argoub, Shatha Al Jaberi, Abdulrahman Alhanbali, Taimein Yacoub, Shahd Rihan, Hala Al-Jaberi, Arriel Benis |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |