Development and validation of machine learning models for 30-day mortality prediction in septic shock
Shanbi Chang, Yuebang Wang, Zijin Zhang, Jian Sun, Juan Wu, Dan Wang, Wenyang Hou, Jun Zhou, Baoyu Wang · 2026
Read Development and validation of machine learning models for 30-day mortality prediction in septic shock by Shanbi Chang, Yuebang Wang, Zijin Zhang, Jian Sun, Juan Wu, Dan Wang, Wenyang Hou, Jun Zhou, Baoyu Wang (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-13561930.txt.
Description
Shanbi Chang and co-authors train machine models to call thirty-day death risk in septic shock. Their 2026 study draws 695 patients from one hospital and feeds age, sex, and thirty-three lab values into the hunt. A shrink step picks the lean set of signals before eleven model kinds compete.
Six lab values survive the cut, led by lactate and platelet count. A random forest model tops the field with a strong score and steady calibration on held-out cases. A web tool turns the model into a bedside risk gauge. One site and no outside check bound the claims, the authors note, so wider trials must come next.
Catalog record
| CREATOR | Shanbi Chang, Yuebang Wang, Zijin Zhang, Jian Sun, Juan Wu, Dan Wang, Wenyang Hou, Jun Zhou, Baoyu Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 37 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |