Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review
Seyedeh Narjes Ahmadizadeh, Hasan Shamsi, Neda Izadi, Ali Dabbagh, Moein Ebrahimi, Marzieh Shahrabi, Fariba Shabani · 2026
Read Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review by Seyedeh Narjes Ahmadizadeh, Hasan Shamsi, Neda Izadi, Ali Dabbagh, Moein Ebrahimi, Marzieh Shahrabi, Fariba Shabani (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-13136600.txt.
Description
Seyedeh Ahmadizadeh and co-authors review AI tools that predict death risk for critically ill children. Pediatric intensive care teams need early, honest risk reads to plan care. Seventeen studies passed the full screening for this review. Most appeared in 2020 or later, showing how fresh the field is.
Random forest was the algorithm used most often across the studies. About eighty-eight percent of models scored above 0.8 on the accuracy curve. The rest fell below that mark, so method choice clearly matters. Bias checks used a standard prediction tool. The authors conclude that algorithm and feature picks drive success more than data volume alone.
Catalog record
| CREATOR | Seyedeh Narjes Ahmadizadeh, Hasan Shamsi, Neda Izadi, Ali Dabbagh, Moein Ebrahimi, Marzieh Shahrabi, Fariba Shabani |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |