Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study
Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu · 2024
Read Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study by Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu (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-11631919.txt.
Description
Zihan Li and co-authors train nine machine learning models to forecast the return of bile duct stones after repeat surgery. Their 2024 study gathers patients from five centers between 2015 and 2020. Recurrent stone disease lacks a trusted forecast tool.
The system, named CARES, aims for dynamic risk calls across five post-surgery years. Two boosting models topped the field, with top scores at or above zero point nine on the standard curve measure. Both did even better on a held-out test set, arguing against overfit. An explanation method ranked instant stone clearance, final clearance, past surgery count, and a tumor marker as the top drivers. An online version of the tool is live.
Catalog record
| CREATOR | Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |